Niels Birbaumer- Breaking the silence: Brain–computer interfaces (BCI) for communication and motor...
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PRESIDENTIAL ADDRESS, 2005
Breaking the silence: Brain–computer interfaces (BCI)
for communication and motor control
NIELS BIRBAUMERa,b
aInstitute of Medical Psychology and Behavioral Neurobiology, University of Tubingen, Tubingen, GermanybNational Institutes of Health, National Institute of Neurological Disorders and Stroke, Human Cortical Physiology, Bethesda, Maryland, USA
Abstract
Brain–computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone.
Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and
noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved
with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in
syndrome with slow cortical potentials, sensorimotor rhythm and P300, and restoration of movement and cortical
reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs
allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At
present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made.
Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force vari-
ations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs,
like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children.
Descriptors: Brain–computer interface, Brain–machine interface, EEG, Invasive brain measures, Locked-in syndrome
A brain–computer interface (BCI) or brain–machine interface
(BMI) activates electronic or mechanical devices with brain ac-
tivity alone. BCIs and BMIs allow direct brain communication in
completely paralyzed patients and restoration of movement in
paralyzed limbs through the transmission of brain signals to the
muscles or to external prosthetic devices. We differentiate inva-
sive from noninvasive BCIs: Invasive BCIs use activity recorded
by brain implanted micro- or macroelectrodes, whereas non-
invasive BCIs use brain signals recorded with sensors outside the
body boundaries.
The brain signals employed for invasive BCIs to date include
(1) action potentials from nerve cells or nerve fibers (Kennedy &
Adams, 2003; Kennedy, Bakay, Moore, Adams, & Goldwaithe,
2000), (2) synaptic and extracellular field potentials (Nicolelis,
2001; Serruya, Hatsopoulos, Paninski, Fellows, & Donoghue,
2002), and (3) electrocorticograms (ECoG; Lal et al., 2005;
Leuthardt, Schalk, Wolpaw, Ojemann, & Moran, 2004). The
noninvasive BCIs used (1) slow cortical potentials (SCP) of the
EEG (Birbaumer et al., 1999), (2) EEG and MEG oscillations,
mainly sensorimotor rhythm (SMR), also called mu-rhythm
(Pfurtscheller, Neuper, & Birbaumer, 2005; Pfurtscheller, Neu-
per, et al., 2003; Wolpaw, Birbaumer, McFarland, Pfurtscheller,
& Vaughan, 2002), (3) P300 and other event-related brain po-
tentials (ERPs; Farwell &Donchin, 1988), (4) BOLD response in
functional magnetic resonance imaging (fMRI; Hinterberger
et al., 2004; Weiskopf et al., 2003; Weiskopf, Scharnowski, et al.,
2005), and (5) near-infrared spectroscopy (NIRS) measuring
cortical blood flow (Coyle, Ward, Markham, McDarby, 2004;
Sitaram et al., in press).
This article reviews the research concerned with invasive and
noninvasive BCIs from the perspective of their clinical usefulness
for communication and motor restauration in paralysis. The re-
views available on invasive BCI in animals (Nicolelis, 2003;
Nicolelis, Birbaumer & Mueller, 2004; Schwartz, Taylor, &
Tillery, 2001) describe primarily the performance of single neuronal
unit resonse patterns for the reconstruction of movement se-
quences in healthy animals; if they discuss clinical applications in
The author and his work are supported by the Deutsche Forschungs-
gemeinschaft (DFG) and the National Institutes of Health (NIH). The
editor, Bob Simons, made invaluable suggestions and corrections at all
stages of themanuscript’s preparation. The comments of two anonymous
reviewers, ofManyDonchin, AndreaKubler, Theresa Vaughan, and Jon
Wolpaw are greatly appreciated. The data frommy laboratory presented
here could not have been realized without a functioning interdisciplinary
research team: the names of the team members appear in the references
cited. They deserve all the credit for this work. The manuscript was
prepared during my stay as a research fellow at the NIH, NINDS,
Bethesda, MD: My friend Leonardo Cohen, M.D., Chief of the Human
Cortical Physiology Section at NINDS, and Cornelia Weber made the
time in Washington, DC, a unique and productive experience.Address reprint requests to: Niels Birbaumer, Ph.D., Institute of
Medical Psychology and Behavioral Neurobiology, MEG-Center, Uni-versity of Tubingen, Gartenstrasse 29, D-72074 Tubingen, Germany.E-mail: [email protected].
Psychophysiology, 43 (2006), 517–532. Blackwell Publishing Inc. Printed in the USA.Copyright r 2006 Society for Psychophysiological ResearchDOI: 10.1111/j.1469-8986.2006.00456.x
517
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human patients at all, a science fiction perspective of whatmay be
possible is given without reference to the few published clinical
applications. The noninvasive BCI literature overviews (Kubler,
Kotchoubey, Kaiser, Wolpaw, & Birbaumer, 2001; Wolpaw
et al., 2002) were based on small numbers of clinical cases;
meanwhile the database for BCI research in clinical populations
has broadened and allows some tentative theoretical and clinical
conclusions not available in previous reviews. Remarkably, the
clinical applications, particular those of BCIs for communication
in completely paralyzed patients, allow a fresh view on some old
and still unresolved theoretical questions in psychophysiology:
1. What is the role of voluntary motor control and of the feed-
back following motor responses in goal directed thinking and
imagery and verbal behavior?
2. What are the consequences of a loss of complete or virtually
complete loss of motor behavior on emotional responding at
the subjective and the physiological level?
3. What is the nature and extent of brain reorganization after
complete cessation of voluntary motor response systems?
What are the consequences of compensatory brain reorgan-
ization on behavior?
This review addresses these questions in the context of BCI re-
search and tries to illustrate once again the usefulness of a union
between clinical and experimental approaches in psychophysiology
for the reformulation of some basic scientific problems in the field.
History of BCI Research
Hans Berger, who discovered the human EEG, speculated in his
first comprehensive review of his experiments with the ‘‘Elek-
trenkephalogramm’’ (1929) about the possibility of reading
thoughts from the EEG traces by using sophisticated mathemat-
ical analyses. Grey Walter, the brilliant EEG pioneer who de-
scribed the contingent negative variation (CNV), often called the
‘‘expectancy wave,’’ built the first automatic frequency analyzer
and the computer of ‘‘average transients’’ with the intention of
discriminating covert thoughts and language in the human EEG
(Walter, 1964). Fetz (1969) published the first paper on invasive
operant conditioning of cortical spike trains in animals. Only the
recent development of BCIs, however, has brought us a bit closer
to the dreams of these pioneers of EEG research.
Invasive and noninvasive BCIs originate from different re-
search traditions, though both have their roots in animal experi-
ments. Invasive BCIs consist of implantedmultielectrode grids in
the motor cortex of paralyzed patients (Donoghue, 2002), pre-
motor cortex of monkeys (Carmena et al., 2003), or parietal
motor command areas (Schwartz et al., 2001). They try to re-
construct intended skilled movements from neuronal firing pat-
terns online. Based on ‘‘sparse coding’’ approaches to motor
learning (Riehle & Vaadia, 2005) and directional coding vectors
of motor neurons (Georgopoulos, Schwartz, & Kettner, 1986),
automatized complex movements can be reconstructed online
from relatively few motor neurons using simple algorithms:
Nicolelis’ group (Carmena et al., 2003) demonstrated in mon-
keys after extensive training of a reaching and grasping move-
ment that firing patterns of 32 neurons are sufficient to execute
that movement directly with an artificial limb. Chapin, Moxon,
Markowitz, andNicolelis (1999) trained rats tomove a lever with
an artificial arm in a Skinner box for reward with extracellular
firing of cortical cells without any actual movement. The neur-
onal firing pattern that used to precede and accompany the lever
pressing response alone was able to operate on the lever deliv-
ering the reward.
Operant Conditioning of Autonomic Functions
The second root of BCI research is intimately tied to the tradition
of biofeedback and instrumental-operant learning of autonomic
functions. During the late 1960s and early 1970s, Neal E. Miller
and collaborators opposed the traditional wisdom of the au-
tonomous nervous system (ANS) as autonomous and independ-
ent of voluntary control of the somatic central nervous system
(CNS). Miller (1969), in a landmark paper in Science, challenged
that view that voluntary control is acquired through operant
(instrumental) conditioning whereas modification of involuntary
ANS functions is learned through classical (Pavlovian) condi-
tioning, a distinction first emphasized by Skinner (1953; Holland
& Skinner, 1961).
Miller presented experimental evidence in curarized and ar-
tificially ventilated rats showing that even after long-term cura-
rization of several weeks, the animals learned to increase and
decrease heart rate, renal blood flow, and dilation and constric-
tion of peripheral arteries in an operant conditioning paradigm
rewarding the animals for increases and decreases of these spe-
cific physiological functions. These studies stirred an enormous
interest in the scientific and clinical community, particularly in
psychosomatic medicine and behavior modification.
The results suggested that instrumental (‘‘voluntary’’) control
of autonomic functions is possible without any mediation of the
somatic-muscular system. Operant training of any internal body
function seemed possible, opening the door for psychological and
learning treatment of many medical diseases such as high blood
pressure, cardiac arrhythmias, vascular pathologies, renal fail-
ure, gastrointestinal disorders, and many others. In the clinic,
biofeedback of these functions replaced the operant conditioning
in rats, the feedback from the specific physiological variable
constituted the reward (for an overview of these years’ enthu-
siasm, see the Aldine series on Biofeedback and Self-Control;
Kamiya, 1971).
During the next two decades, Miller and his students at
Rockefeller University tried to replicate their own findings. Fig-
ure 1 shows the steady decline of the size of the conditioning
effect with each replication. Finally, by the mid-1980s, it was
impossible to replicate the previous effects. Barry Dworkin, Neal
Miller’s last andmost prolific student, continued to try and build
the most sophisticated ‘‘intensive care unit’’ for curarized rats,
but again, operant training of autonomic function or nerves in
the curarized rat was impossible.
In contrast, classical conditioning succeeded even in single
facial nerve fibers (Dworkin, 1993; Dworkin & Miller, 1986).
Dworkin attributed the failure of operant techniques to the
missing homeostatic effect of the reward: The reward acquires its
positive effect through homeostasis-restoring effects (i.e., inges-
tion of food restores glucostatic and fluid balance). In the cu-
rarized rat (and the completely paralyzed respirated and fed
patient?), where all body functions are kept artificially constant,
the homeostatic function of the reward is no longer present be-
cause imbalances of the equilibrium do not occur.
The chronically curarized rat and the completely paralyzed,
artificially ventilated and fed locked-in patient share many simi-
larities; difficulties in communicating with these patients may be
understood based on these similarities.
518 N. Birbaumer
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The difficulties in replicating the operant learning of auto-
nomic variables were accompanied by an ‘‘awakening’’ in the
clinical arena of biofeedback applications: The most impressive
clinical results were achievedwith electromyographic feedback in
chronic neuromuscular pain (Flor & Birbaumer, 1993), neuro-
muscular rehabilitation of various neurological conditions
(Birbaumer & Kimmel, 1979), particularly external spincter
control in enuresis end encopresis (Holzl & Whitehead, 1983),
and posture control in kyphosis and scoliosis (Birbaumer, Flor,
Cevey, Dworkin, &Miller, 1994; Dworkin et al., 1985), but there
were clinically unimpressive or negligible results in essential
hypertension (Engel, 1981; McGrady, Olson, & Kroon, 1995),
heart rate (Cuthbert, Kristeller, Simons, Hodes, & Lang, 1981),
and gastric hyperfunction (Holzl &Whitehead, 1983). It became
painfully clear that only very limited positive effects of bio-
feedback on visceral pathology with clinically and statistically
relevant changes occur. There was one notable exception,
however: neurofeedback of brain activity (Elbert, Rockstroh,
Lutzenberger, & Birbaumer, 1984).
Seizure Control
The most spectacular and popularized results in the emerging
field of biofeedback (or ‘‘physiological regulation’’ as it is pres-
ently called) were the self-regulation of brain waves (Kamiya,
1971). Increase and decrease of alpha frequency of the EEGwere
supposed to create ‘‘meditative’’ states with many beneficial ef-
fects in the periphery and on behavior. Theta wave augmentation
and reduction had profound effects on vigilance and attention
(Birbaumer, 1977). Slow cortical potentials (SCP) control allowed
anatomically specific voluntary regulation of different brain areas
with area specific effects on behavior and cognition (for an over-
view, see Rockstroh, Elbert, Birbaumer, & Lutzenberger, 1989).
Warning voices such as experiments byMulholland and his group
(Mullholland & Evans, 1966) demonstrating perfect control of
alphawaves throughmanipulation of the oculomotor system and
decoupling of eye fixation went largely unheard.
Sterman (Sterman, 1981; Sterman& Friar, 1972) was the first
to propose self-control of epileptic seizures (Elbert et al., 1984) by
an augmentation of sensorimotor rhythm (SMR). SMR in
human subjects is recorded exclusively over sensorimotor areas
with frequencies of 10 to 20 Hz and variable amplitudes.
Pfurtscheller and colleagues (2005) localized the source of human
SMR in the sensorimotor regions following the homuncular or-
ganization of the motor and somatosensory cortical strip. Im-
agery of hand movement abolishes SMR over the hand region;
imagery or actual movement of the legs blocks SMR in the
interhemispheric sulcus. Pfurtscheller called this phenomenon
event-related desynchronization and synchronization (Pfurtsc-
heller et al., 2005).
On the basis of careful animal experiments (Sterman and
Clemente, 1962a, 1962b), Sterman demonstrated incompatibility
of seizures in motor and premotor areas in the presence of SMR.
Cats exhibited maximum SMR during motor inhibition and
various sleep stages. Presence of spindles during different sleep
stages, particularly during rapid eye movement (REM) sleep in-
dicated recruitment of inhibitory thalamo-cortical circuits and
blocked experimentally induced seizures. Sleep spindles and
SMR share identical physiological mechanisms. Epileptic cats
and humans were trained to increase SMR, and, after extensive
training ranging from 20 to more than 100 sessions, Sterman
(1977) was able to demonstrate seizure reduction and complete
remission in some patients with drug-resistant epilepsy. It is im-
portant to note that SMR is often called mu-rhythm following a
suggestion of Gastaut (Gastaut, 1952; Gastaut, Terzian, & Gas-
taut, 1952) who noted its abolition in some types of seizures.
However, it is not clear whether the neurophysiological bases of
the two phenomena are really comparable and therefore I rec-
ommend that the term SMRas used by Sterman et al. be retained
because of its well-defined theoretical and experimental back-
ground.
It is not accidental that SMR operant control is achieved
through activation and deactivation of the central motor loops.
Again, successful voluntary regulation of a physiological variable
is tied to the regulation of the motor system. The results of SMR
control in animals and patients seem to demonstrate that ma-
nipulation (mediation) of the peripheral motor efferents is not a
necessary requirement of SMR control, at least on the basis of
EMG recordings of the arm muscles showing no measurable
variation during motor imagery with central nervous system
event-related desynchronization (Pfurtscheller et al., 2005). The
successful brain regulation of SMR in completely paralyzed
patients reported below confirms that changes of the peripheral
motor system do not mediate CNS activity responsible for SMR
origin. The notion of the critical role of CNS activity in voluntary
action and thought remains.
Beginning in 1979, our laboratory published an extensive
series of experiments that demonstrated operant control of slow
cortical potentials in the EEG. These demonstrations differed
from previous brain biofeedback work as they documented the
following in well-controlled experimental paradigms:
1. Strong and anatomically specific effects of self-induced cor-
tical changes on behavior and cognition;
2. Solid neurophysiological evidence about anatomical sources
and physiological function of slow cortical potentials (for re-
views, see Birbaumer, 1999; Birbaumer, Elbert, Canavan, &
Rockstroh, 1990; Birbaumer, Flor, Lutzenberger, & Elbert,
1995; Birbaumer, Roberts, Lutzenberger, Rockstroh, &
Elbert, 1992).
Of particular interest in the context of CNS motor mediation of
voluntary control of brain activity was the fact that SCPs
Breaking the silence 519
Figure 1. Effects of operant learning of heart rate control in the curarized
rat rewarded with intracranial rewarding brain stimulation (triangles)
and shock avoidance (circles). Replications of the same experiment from
1966 to 1970 (from Dworkin & Miller, 1986).
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originating from posterior parietal sources were resistant to
operant learning whereas central and frontal SCPs could be
brought under voluntary, operant control after one to five train-
ing sessions (Lutzenberger, Roberts, & Birbaumer, 1993). Sev-
eral clinical studies confirmed the critical importance of the
anterior brain systems for physiological regulation of CNS func-
tions: Lutzenberger et al. (1980) showed that patients with ex-
tended prefrontal lobe lesions were unable to learn SCP control
despite intact intellectual functioning. Disorders with prefrontal
dysfunctions such as attention deficit disorder (ADD; Birbau-
mer, Elbert, Rockstroh, & Lutzenberger, 1986) and schizophre-
nia (Schneider et al., 1992) exhibited extreme difficulties in
acquiring SCP control, and attentional improvement after SCP
or SMR neurofeedback training required long training periods
(Strehl, Leins, Goth, Klinger, & Birbaumer, in press). Again,
peripheral motor function played no role in SCP conditioning
(Birbaumer & Kimmel, 1979), but intact prefrontal systems
seemed to be a prerequisite for successful brain control. Figure 2
shows the results of a study where healthy subjects learned SCP
control, and fMRI (BOLD response) was recorded simultan-
eously during training.
Subjects received visual feedback of positive and negative
SCPs of 6 s duration and were rewarded for the production of
target amplitudes (Hinterberger et al., 2004; Hinterberger,
Birbaumer, & Flor, 2005; Hinterberger, Veit, et al., 2005). As
illustrated in Figure 2, successful voluntary brain control de-
pends on activity in premotor areas and the anterior parts of the
basal ganglia. Birbaumer et al. (1990) had proposed earlier that
physiological regulation of SCP and attention depends critically
on anterior basal ganglia activity regulating local cortical acti-
vation thresholds and SCP in selective attention and motor
preparation. Braitenberg (Braitenberg & Schuz, 1991) created
the term ‘‘thought pump’’ (‘‘Gedankenpumpe’’ in German) for
this basal ganglia–thalamus–cortical loop. Taken together, the
extensive literature on the SCP also suggests that operant-vol-
untary control of local cortical excitation thresholds underlying
goal-directed thinking and preparation depends on an intact
motor or/and premotor cortical and subcortical system.
Encouraged by the reliable and lasting effects of brain self-
regulation on various behavioral variables and by Sterman’s case
demonstrations, Birbaumer and colleagues conducted several
controlled clinical studies on the effect of SCP regulation on
intractable epilepsy (Kotchoubey et al., 2001; Rockstroh et al.,
1989, 1993). Based on their neurophysiological model of SCP
regulation, patients with focal epileptic seizures were trained to
down-regulate cortical excitation by rewarding them for cortical
positive potentials and perception of SCP changes. After ex-
tremely long training periods, some of these patients gained close
to 100% control of their SCPs and seizure suppression, tempting
Birbaumer and colleagues to apply cortical regulation as a BCI
for paralyzed patients: Given that epileptic patients suffering
from a dysregulation of cortical excitation and inhibition and
consequent brain lesions learn to control their brain responses
both within the laboratory and in daily life, it is not unreasonable
to ask whether a paralyzed patient could learn to activate an
external device or computer in order to move a prosthetic arm or
to convey messages to a voice system.
Noninvasive BCIs for Communication in Paralysis
Amyotrophic Lateral Sclerosis (ALS) is a progressive motor
disease of unknown etiology resulting in a complete destruction
of the peripheral and central motor system but only affecting
sensory or cognitive functions to a minor degree (Norris, 1992).
There is no treatment available; patients have to decide to accept
artificial respiration and feeding after the disease destroys re-
spiratory and bulbar functions for the rest of their life or to die of
respiratory problems. If they opt for life and accept artificial
respiration, the disease progresses until the patient loses control
of the last muscular response, which is usually the eye muscle or
the external sphincter. The resulting condition is called com-
pletely locked-in state (CLIS). If rudimentary control of at least
one muscle is present, we speak of a locked-in state (LIS). Other
conditions leading to a locked-in state are subcortical stroke and
other extended brain lesions, Guillain-Barre syndrome, some
rare cases of Parkinson disease, and Multiple Sclerosis.
520 N. Birbaumer
Figure 2. Effects of self-regulation of slow cortical potentials (SCP) on regional metabolic changes measured with fMRI. Left:
BOLD responses during self-produced cortical negativity (left column) and positivity (right column). Red colored brain areas
indicate activation, green color deactivation. Right: A: Activation of anterior basal ganglia during self-induced cortical positivity. B:
Related deactivation of premotor areas during cortical positivity (from Hinterberger, Veit, et al., 2005).
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Based on the extensive knowledge and clinical experience ac-
quired with SCP control, Birbaumer et al. (1999) developed a
BCI system for ALS patients. As in the epilepsy studies, patients
were first trained to produce positive or negative SCPs upon the
command of an auditory cue. They watched their SCP changes
or, in case of insufficient vision, received auditory feedback and
reward for target amplitude changes (Kubler, Kotchoubey, et al.,
2001; Kubler, Neumann, et al., 2001). After achievingmore than
70% control, letters or words are presented on a computer screen
or spoken by a word program. Patients select a letter by succes-
sively reducing letter strings containing the desired letter by cre-
ating SCPs after appearance of the desired letter (Birbaumer
et al., 1999; Birbaumer, Hinterberger,Kubler, &Neumann, 2003;
Kubler, Kotchoubey, et al., 2001; Perelmouter & Birbaumer,
2000; Tregoubov & Birbaumer, 2005; Wolpaw et al., 2002).
Thirty-two patients with ALS at various stages of their disease
were trained to use the SCP-BCI. Eventually, seven of these pa-
tients arrived at the locked-in state and were able to continue to
use the BCI. Seven additional patients began training after en-
tering the complete locked-in state; none of them achieved lasting
BCI control and communication. One of these CLIS patients
communicated shortly with a pH-based communication system
but lost this control after two sessions (Hinterberger, Birbaumer,
et al., 2005; Wilhelm, Jordan, & Birbaumer, 2006).
The SCP-BCI needs long training periods, sometimes
months, in the home of the patient (all patients were artificially
respirated and paralyzed), and letter selection speed is slow,
usually one minute per letter. However, speed is not an issue in
artificially respirated paralyzed patients devoting all their cog-
nitive and emotional energies to communication (Birbaumer,
Strehl, & Hinterberger, 2004). The SCP-BCI needs professional
attention and continuous technical support; easy application by
family members or nonprofessional caretakers was possible in
only one patient.
Wolpaw and colleagues at the Wadsworth Laboratories at
Albany, NewYork, did an extensive series of experiments mainly
with healthy persons using SMR rather than SCP as the target
brain response (Wolpaw et al., 2002). In a group of patients, two
with high spinal cord lesions, Wolpaw and McFarland (2004)
demonstrated that multidimensional control of a cursor move-
ment on a computer screen can be learned in just a few sessions of
training: The subjects were able to move a cursor within 10 s into
one of eight goals appearing randomly at one of the four corners
of the screen. The flexibility, speed, and learning performance is
generally equal to that seen when invasive multielectrode BMI
systems are tested in animals. The Wolpaw and McFarland
(2004) preparation consisted of a simple electrode montage cov-
ering the hand and foot area with a linear online filtering and
detection algorithm used for data reduction and quantification.
Most subjects employed right and left hand and feet imagery to
reach the target goals in SMR-BCI.
The Albany and Tubingen group joined forces in an NIH-
funded project and compared the feasibility and performance of
the SCP-BCI, the SMR-BCI, and the P300-BCI developed by
Farwell and Donchin (1988) in seven pre-LIS ALS patients in a
balanced within-subject design. The results were clear-cut: All
patients achieved sufficient performance rates (more than 70%of
the trials correct) after 20 sessions with SMR-BCI training, four
of the seven could spell with the P300-BCI, but none of the
patients achieved acceptable performance rates with the SCP-
BCI despite significant differentiation between negative and
positive SCP. It can be concluded that in ALS patients with
functioning vision and eye control, SMR-BCI and P300-BCI
shows the most promising results. The project continues to fol-
low these patients into complete paralysis and eventually into the
complete locked-in state. Figure 3 gives examples of the training
situations for the three BCIs.
SCP-BCIs need more extensive training than other BCI
modes but may have the best stability and independence of sens-
ory, motor, and cognitive functioning necessary for its applica-
tion to the LIS and the CLIS patients. The patients described
earlier (Birbaumer et al., 1999) had high success rates with SCP-
BCI training but only after many more sessions.
Together with the introduction of controlled clinical trials to
document comparative BCI performance, the Albany–Tubingen
group created aWeb site, BCI 2000 (http://www.bciresearch.org/
BCI2000/bci2000.html; Schalk, McFarland, Hinterberger,
Birbaumer, & Wolpaw, 2004) providing free software modules
for BCI applications in research and clinic. More than 100
laboratories are now regular contributors to the BCI 2000 Web
site, improving both the hardware and software modules. The
aim is an inexpensive, FDA and CE approved, easy-to-use,
universal, noninvasive BCI that will allow SCP, SMR, P300, and
other possible oscillatory brain activities (i.e., gamma band in
ECoG) in a world wide net of participants whose data collection
and analysis will contribute to the continuous improvement and
validity of BCI applications.
Long training periods, noisy signals, the continuous profes-
sional attention necessary, slow spelling speed, electrode and skin
problems with long recording times, and the controlled attention
focus during spelling makes the invasive BCI approach an at-
tractive alternative, at least at a theoretical level.
Invasive BCIs for Communication
Kennedy, Kirby, Moore, King, and Mallory (2004) published
several single cases with ALS in different stages (none either LIS
or CLIS), with a cortically implanted glass microelectrode filled
with a neurotrophic growth factor. The axon of the cell targeted
by the electrode grows into it and allows recording of the spike
activity. Some of the patients learned to spell using the spike
activity mainly by turning it on and off in a ‘‘yes’’ or ‘‘no’’ fash-
ion. From the published material, it is difficult to judge the use-
fulness of this preparation because death and medical
complications interrupted communication in several cases (one
case reportedly used the device on a more continuous basis).
None of the patients were in urgent need of the device because all
had rudimentary motor control.
Brunner, Graimann, Huggins, Levine, and Pfurtscheller
(2005), Graimann, Huggins, Levine, and Pfurtscheller (2004),
and Pfurtscheller, Mueller, Pfurtscheller, Gerner, and Rupp
(2003) implanted subdural electrodes in presurgical epileptic pa-
tients and demonstrated that control of SMR synchronization
and desynchronization can be achieved in one to several sessions.
Spelling was not required.
More than 100 scientists attending the 2005 BCI conference in
Rennselearville, New York, were asked for their opinion on the
future of BCI applications. The majority of the BCI researchers
present at the conference believed that the noninvasive BCI
showed the most promise for development during the next dec-
ade. The main argument against noninvasive BCIs was their
limited capacity to represent more than two signal alternatives
(‘‘yes,’’ ‘‘no,’’ ‘‘select,’’ ‘‘ignore,’’ etc.), and this limitation would
Breaking the silence 521
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prohibit their use formotor restoration ormotor neuroprosthesis
applications (Carmena et al., 2003; Taylor, Tillery, & Schwartz,
2002). This argument was recently countered experimentally by
Wolpaw and McFarland (2004), who demonstrated two-dimen-
sional cursor control over the sensorimotor rhythm of the scalp
EEG. Even high-level motor control of complex movements
combined with sophisticated prosthesis design can be exerted
with a two-dimensional command system. In earlier papers by
Elbert et al. (summarized in Birbaumer et al., 1990), healthy
participants were trained to produce differential frontal, central,
parietal, and left-right hemispheric negative and positive slow
cortical potential shifts, allowing them at least several degrees of
freedom for cursor or prosthesis control (see Birbaumer at al.,
1990, for a review).
522 N. Birbaumer
Figure 3. Three types of BCIs. A: BCI using slow cortical potentials (SCP depicted at the top). Patient selects one letter from the
letter string on screen (right below) with positive SCPs, the spelled letters appear on top of the screen. B: SMR-BCI. Top right: SMR
oscillations from sensorimotor cortex during inhibition ofmovement and imagery or execution ofmovement (EEG trace below). On
the left part of the picture is the feedback display with the target goal on the right side of the screen indicating the required SMR
increase (target at bottom) or SMR decrease (target at top). The curser reflecting the actual SMR is depicted in redmoving from the
right side of the screen toward the target goal. C: P300-BCI. Rows and columns of letter strings are lighted in rapid succession.
Whenever the desired letter (P) is among the lighted string, a P300 appears in the EEG (after Sellers & Donchin 2006; Piccione et al.
2006).
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A further argument against widespread use of noninvasive
BCIs for motor control and communication consists of the long
training periods required and the high error rates that are ob-
served even after extensive training. Patients often need weeks to
learn to produce a particular brain response voluntarily in order
to select letters or words reliably above chance. Although healthy
persons may achieve brain control within one or two sessions,
patients need a minimum of 20 sessions to achieve more than
70% correct selections at least with sensorimotor rhythm or slow
cortical potentials (Kubler, Nijboer, et al., 2005). The incorpor-
ation ofmore sophisticated algorithms for EEG classification did
not improve the situation substantially (Hinterberger, Kubler,
Kaiser, Neumann, & Birbaumer, 2003; see results of the BCI
competition in the IEEE Transactions in Biomedical Engineer-
ing; Nicolelis et al., 2004). Papers by Hinterberger, Veit, et al.
(2003) and Piccione et al. (2006) illustrate this point nicely; they
report equivalent results for BCI control with different classifi-
cation algorithms (Hill et al., in press).
In humans, there are two published reports, in addition to the
alreadymentioned attempts by Pfurtscheller’s group, on invasive
BCIs with epileptic patients. In these experiments, subdural
macroelectrodes were implanted over frontal regions, and pa-
tients attempted spelling or they performed imagery tasks (Lal et
al., 2005; Leuthardt et al., 2004). In a single session with these
patients, it was possible to differentiate imagination of hand,
tongue, and mouth movement using the ECoG. Figure 4 shows
the perfect nonoverlapping classification of hand and tongue
movements at the sensorimotor cortex (Support Vector Ma-
chines, SVM,were used as classification algorithms; see Lal et al.,
2004; Schroder et al., in press), allowing the patient to select
letters at a speed of several letters per minute after a 20-min
training session. Patients spelled by selecting letters with imagery
of finger movement (green field at cortex in Figure 4) and re-
jecting a letter by imagery of tongue movement (red field at cor-
tex of Figure 4).
This indicates, not surprisingly, that with subdurally implant-
ed macroelectrodes, degrees of freedom, precision of classifica-
tion, and success rates may substantially improve. The first
implantation of 100microelectrodes in themotor cortex of a high
spinal cord patient by Donoghue et al. (personal communica-
tion) and Hochberg, Mukand, Polykoff, Friehs, and Donoghue
(2005) seems to allow improved BCI performance. However, of
17 ALS patients in our sample, all in the final stage of the disease
and all artificially respirated and fed, only 1 agreed to implant-
ation of subdural macroelectrodes (Wilhelm et al., 2006). Even
when informed about the possibilities and advantages of the
surgical implantation, 16 patients refused the procedure and
preferred the slow and error-prone noninvasive device. An im-
portant argument of patients was that time is not an issue if one is
completely paralyzed (Birbaumer et al., 1999, 2004; Kubler et al.,
2003; Kubler, Nijboer, et al., 2005).
It is fair to conclude, therefore, that noninvasive BCIs using
different types of EEG signals such as slow cortical potentials,
P300, or SMR oscillations at present are and will remain the
method of choice for communication in paralyzed and hopefully
also in completely locked-in patients with ALS and other debili-
tating neurological diseases (subcortical stroke, Guillain Barre,
extensive brain damage). If patients, their families, and the local
ethical committees agree, implantations of micro- or macroelec-
trodes subdurally or in brain tissue should be considered. How-
ever, the database of invasive BCIs for communication purposes
in paralyzed patients at present is too small to judge their efficacy,
and the willingness of patients and their families to agree to im-
plantation is weak as long as the noninvasive BCIs are available
Breaking the silence 523
Figure 4. Support-vector-machine (SVM) classification of electrocorticogram (ECoG) of a presurgically implanted 64-electrode
grid over frontal cortex. Patient imagined finger movement to select a letter (indicated by the finger on the screen, lower part of
figure, left) and tongue movement to reject a letter (indicated by Einstein’s tongue, lower right). Upper part: classification result for
all frequencies from 7 to 100Hz. Red shows the classification for tongue imagery, green for finger projected on the cortical surface of
the same patient.
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and functioning. The slow spelling speed and high error rate (even
in highly trained patients rarely above 80% trials correct) of non-
invasive EEG-based BCIs is well tolerated by paralyzed patients
with adifferent life perspective and anurgent need to communicate.
Operant Learning, Thinking and BCI Control in the Complete
Locked-in State
As mentioned above, none of the ALS patients starting BCI
training after entering the complete locked-in state acquired sta-
bile communication (n5 17). Again, one of these patients was
implanted with subdural electrodes over the left frontal cortex.
Despite clean ECoG recordings and extensive learning attempts
over several weeks, no communication was achieved.
The most frequent argument explaining the lack of commu-
nication in the complete locked-in state assumes that with pro-
gression of ALS or Guillain-Barre Syndrome deterioration of
cognitive functions prevents learning and communication (see
Sellers & Donchin, 2006, for a discussion of the problem). It is
difficult to reject this argument empirically because neuropsy-
chological testing for cognitive functioning is impossible in a
completely paralyzed person. We therefore developed an ERP
test with an extensive series of cognitive experimental paradigms
ranging from simple oddball-P300-evoking tasks to highly com-
plex semantic mismatch N400 and personalized memory tasks
eliciting late cortical positivities (Hinterberger, Birbaumer, et al.,
2005; Kotchoubey et al., 2005).
More than 100 patients in responsive and nonresponsive
vegetative state and 24 ALS patients at different stages of the
disease were tested. The relationships between the complexity of
a cognitive task and the presence or absence of a particular
component are rather inconsistent (Kotchoubey et al., 2005;
Kotchoubey, Lang, Bostanov, & Birbaumer 2002), meaning a pa-
tient may show absent early cortical components such as N1 but
normal P300, or absent P300 to simple tones but intact P600 to
highly complex verbal material. With one exception, all CLIS pa-
tients had ERP responses to one or more of the complex cognitive
tasks, indicating at least partially intact processing stages in the
complete locked-in state (Hinterberger et al., 2005). Patients in the
more advanced stages of ALS show slowing of waking EEG some-
times into the theta band. This slowing may be, at least in part,
caused by episodes of anoxia due to inadequate functioning of
artificial respiration. It is oftendifficult to decidewhether the patient
is awake or in sleep stage 1 or 2. One CLIS patient gave informed
consent to implantationof electrodes in the brain over a two-session
period by answering ‘‘yes’’ with imagery of milk taste and ‘‘no’’ by
imagining lemon taste, and measurement of the pH level in mouth
cavity mucosa served as the dependent variable (Wilhelm et al.,
2006). Responding with BCI and the pH device was lost again after
implantation in this patient. Slowing of the ECoG and complete
absence of gamma-band activity characterizes the recordings.
These ERP data neither prove nor disprove normal informa-
tion processing in CLIS but suggest some intact ‘‘processing
modules’’ in most ALS patients with CLIS despite a reduced
general arousal. Three of the remaining 12 patients of our sample
entered LIS and continued to use the SCP-BCI for verbal com-
munication, indicating transfer of learning from rudimentary
motor control (mostly eye movements) to LIS and probably to
CLIS also.
Assuming partially intact processing in ALS patients who are
completely locked in and possible transfer of already acquired
BCI communication to CLIS, the question of why the patients
who entered the CLIS before learning BCI use did not acquire
control of their brain signals (SCP-BCI and SMR-BCI was tried
on this CLIS group) remains. Figure 1 demonstrating the failure
to replicate operant (‘‘voluntary’’) learning of visceral functions
(see Dworkin & Miller, 1986) may provide an answer to this
question: Chronically curarized rats and people with longer time
periods in CLIS may lose the contingency between the required
physiological behavior (SMRdecrease or heart rate increase) and
its consequences (brain stimulation reward in the curarized rat
and letter selection in the patient). Extinction sets in due to there
being so few reinforced learning trials in the rat and in the
completely locked-in patient. No contingency remains at all:
Thoughts and intentions are never followed by their anticipated
consequences in one’s own behavior or in the behavior of others,
and thoughts and imagery and goal-directed feelings are extin-
guished.
Theories of consciousness come to a conclusion similar to
learning theory accounts of extinction of thinking. In a Hebbian
tradition, associative binding between distinct stages of neural
activity was postulated as the crucial mechanism behind con-
scious experience and perception of sensory and motor events
(Singer & Gray, 1994/1995). The presence of localized gamma-
band responses in the cortex functions as an electrophysiological
indicator of associative binding of cell assemblies intomeaningful
percepts; its absence seems incompatible with conscious percepts
and ‘‘Gestalt’’ formation (Kaiser, Lutzenberger, Preissl, Acker-
mann, & Birbaumer, 2000). Psychophysiological and psycho-
physical experiments comparing self-induced voluntary actions
with the same but involuntary movements caused by transcranial
magnetic stimulation (TMS) or external agents demonstrate that
conscious decision and perception of ‘‘will’’ depends on the close
contiguity in time between the decision and the response. Vol-
untary action and thoughts and their consequences are attracted
together in time; involuntary externally initiated and attributed
responses and their effects are experienced asmore distant in time
(Haggard, Clark, & Kalogeras, 2002; Libet, Gleason, Wright, &
Pearl, 1983). They are consequently not interpreted as a conscious
unit but separate cognitive elements incapable of acquiring any
contextual meaning. Virtually all thought–action–consequence
contingencies in a completely paralyzed person become externally
induced by patient-independent agents, usually the caretakers. The
resulting cognitive state and remaining information-processing ca-
pacities remain unclear until the first CLIS patient communicates.
Under the assumption that passive-sensory information pro-
cessing remains intact in completely locked-in patients (see
above), the failure to control autonomic functions with operant
learning in the curarized rat (see Dworkin & Miller, 1986) and
the described experiments on transcranial magnetic stimulation
and voluntary movement seem to provide converging evidence
for the following: In the complete locked-in state, the fact that
intentional thoughts and imagery are rarely followed by a re-
warding or punishing stimulus (i.e., attention from others for
that thought) creates an extension of the subjective time percep-
tion of the interval between a response (thought) and eventual
consequences. Therefore, the probability for an external event
(e.g., attention of a family member) to function as a perception of
a causal contingency between the response (thought) and its
consequence becomes progressively smaller, and after a long
CLIS it may vanish altogether. What fills the subjective world
may consist only of the few remaining external auditory and
tactile and visceral sensations bearing no contextual relationship
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between them. With the lack of reinforcing contingencies con-
trolling the maintenance of the stream of thoughts, they extin-
guish slowly. As demonstrated by Haggard et al. (2002), it is this
lack of motor control consisting of intention (‘‘will’’), prepar-
ation, execution, and sensory and external feedback that deter-
mines the deteriorating subjective time estimation between
response and its consequence.
Donchin (personal communication) assumes that ‘‘fooling’’ the
system by providing artificial stimulation such as TMS or electric
brain stimulation contingent after a particular neural respose may
delay the extinction of goal-directed thinking. The motor control
factor responsible for the cessation of voluntary cognitive activity
and goal-directed thinking in the completely locked-in patient
and the curarized animal lends support to a ‘‘motor theory of
thinking’’ similar to that discussed by William James (1890).
Another consequence of response–consequence separation
was described as ‘‘learned helplessness’’ that characterized de-
pression at the affective level and deficits in problem solving at
the cognitive level (Seligman, 1975). Surprisingly, the common-
sense prediction that complete paralysis accompanied by the loss
of most positive reinforcers should result in depression and des-
pair was not confirmed. But common sense and folk psychology
often result in egregious errors.
Emotion and Quality of Life in ALS and Paralysis
Most ALS patients opt against artificial respiration and feeding
and die of respiratory problems. In many countries, doctors are
allowed to assist the transition with sedating medication to ease
respiration-related symptoms. If doctor-assisted suicide or eu-
thanasia is legal, as it is in the Netherlands and Belgium, very few
patients vote for continuation of life. The vast majority of family
members and doctors (usually neurologists) believe that the
quality of life in total paralysis is extremely low and continuation
of life constitutes a burden for the patient and that it is unethical
to use emergency measures such as tracheostomy to continue life.
The pressure on the patient to discontinue life is enormous.
The facts on end-of-life issues and quality of life do not sup-
port hastened death decisions in ALS, however, and the scientific
literature and our own studies challenge the pervasive myth of
helplessness, depression, and poor quality of life in respirated and
fed paralyzed persons, particular with ALS (Albert, Rabkin, Del
Bene, Tider, &Mitsumoto, 2005; Quill, 2005). Most instruments
measuring depression and quality of life such as the widely used
Beck or Hamilton depression scales are invalid for paralyzed
people living in protected environments because most of the
questions do not apply to the life of a paralyzed person (‘‘I usu-
ally enjoy a good meal,’’ ‘‘I like to see a beautiful sunset’’). Spe-
cial instruments had to be developed for this population (Kubler,
Winter, et al., 2005). In studies by Breitbart, Rosenfeld, and
Penin (2000) and by our group (Kubler,Winter, et al., 2005) only
9% of the patients showed long episodes of depression, most of
them in the time period following the diagnosis and a period of
weeks after tracheostomy. Figure 5 shows the results for depres-
sion (A) and for quality of life (B) rated by patients and family
members and caretakers. As can be seen, ALS patients are not
clinically depressed. In fact, they are in a much better mood than
psychiatrically depressed patients without any life-threatening
bodily disease. Likewise, patients rate their quality of life asmuch
better than their caretakers and family members do, even when
these patients are completely paralyzed and respirated. None of
the patients of our sample (some of them in LIS) requested has-
tened death.
It could be argued that questionnaires and interviews reflect
more social desirability and social pressure than the ‘‘real’’ be-
havioral–emotional state of the patient. The social pressure in
ALS, however, directs the patient toward death and interruption
of life support. The data, therefore, may underestimate the posi-
tive attitude in these groups. This hypothesis is strongly sup-
ported by a series of experiments with ALS patients at all stages
of their disease using the International Affective Picture System
(IAPS; Lang, Bradley, & Cuthbert, 1999). Lule et al. (2005) and
Lule et al. (in press) using a selection of pictures with social
content, found more positive emotions to positive pictures and
less negative ratings to negative pictures in ALS than in matched
healthy controls. Evenmore surprising are the brain responses to
the IAPS slides (Figure 6). FMRI measurement in 13 patients
with ALS and controls demonstrated increased activation in the
supramarginal gyrus and other areas responsible for empathic
emotional responses to others comparable to the ‘‘mirror neuron
network’’ identified first by Rizolatti and colleagues (Gallese,
Keysers, &Rizzolatti, 2004). Furthermore, brain areas related to
the processing of negative emotional information such as the
anterior insulae and amygdala show less activation in ALS.
These differences become stronger with progression of the dis-
ease 6 months later.
One is tempted to speculate that with progression of this fatal
disease, emotional responding on the behavioral and central
nervous system level improves toward positively valenced social
cues, resulting in a more positive emotional state than in healthy
controls! The positive responding and positive interaction of the
social environment and caretakers to a fatally ill, paralyzed per-
son may, in part, be responsible for the prosocial emotional be-
havior and for the modified brain representation of the
‘‘observer’’ depicted in Figure 6 as predicted by social learning
theory (Bandura, 1969). Taken together, the results on emotional
responding and quality of life in paralyzed ALS patients suggest
a more cautious and ethically more responsive approach toward
hastened death decisions and last-will orders of patients and their
families. The data reported here also speak pervasively for the
usefulness and necessity of noninvasive BCI in ALS and other
neurological conditions leading to complete paralysis.
The preceding sections were devoted to BCIs designed for
verbal communication in completely paralyzed persons unable to
use muscular or autonomic responses to activate an assisted
communication device. The second major field of BCI research
concerns restoration of movement in patients with paralysis,
mostly spinal cord lesions, chronic stroke, and other movement
disorders. It is certainly an attractive possibility to build a direct
connection between voluntary movement command centers in
the brain and the periphery isolated from these regions by a
central, spinal, or peripheral lesion.
Invasive and Noninvasive BCIs for Restoration of Movement
Brain–computer interface research received its impetus from an-
imal research reconstructing movement from microelectrode-
recorded spike trains or synaptic field potentials (Donoghue,
2002; Nicolelis, 2001). After extensive training and the imple-
mentation of learning algorithms (for an exception, where
animals learned rapidly, see Serruya et al., 2002), monkeys
move cursors on screens toward targets or an artificial hand
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moves in four directions directed by spike activity, demonstrating
the possibility of translating cellular activity into simple move-
ments online. After such training, even complex movement pat-
terns can be reconstructed from an astonishingly small number of
cells located in the motor or parietal areas (Musallam, Corneil,
Greger, Scherberger,&Andersen, 2004;Nicolelis, 2001; Schwartz
et al., 2001; Taylor et al., 2002). The plasticity of the cortical
circuits allows learned control of movements directly from the
cellular activity even outside the primary or secondary hom-
uncular representations of the motor cortex (Taylor et al., 2002).
A multielectrode array recording spike and field potentials
simultaneously was implanted in a single quadriplegic patient’s
526 N. Birbaumer
Figure 5. Depression and quality of life in ALS. A: Depressionmeasured with amodified version of the Beck Depression Inventory
in healthy controls, ALS patients at different stages of their disease, and psychiatrically depressed patients. ALS patients are
significantly more depressed than normals but within the normal range. B: Quality of life in different dimensions of daily living for
ALS patients (white bars) and their significant others (green, usually family members). (From Kubler, Nijboer, et al., 2005.)
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motor hand area (2004) by Donoghue’s group (personal com-
munication, April 2005). Within a few training sessions, the pa-
tient learned to use neuronal activity from field potentials to
move a computer cursor in several directions comparable to the
tasks used for multidimensional cursor movements in the non-
invasive SMR-BCI reported by Wolpaw and McFarland (2004).
None of the invasive procedures allowed restoration of skillful
movement in paralyzed animals or people in everyday-life situ-
ations. The animals studied in BMI research (Nicolelis, 2003)
were all intact animals who learned to move an artificial device
or curser for food reward without moving their intact arm
in highly artificial laboratory situations. Any generalization
from the invasive animal BCI approach to paralyzed people is
premature.
In contrast to the invasive approaches, SMR-controlled BCIs
developed by Pfurtscheller and colleagues (Pfurtscheller, Neu-
per, et al., 2003; Pfurtscheller et al., 2005) allowed control of
reaching and grasping in high spinal cord lesioned patients. Pfu-
rtscheller, who was the first in testing and implementing SMR-
based BCIs for motor paralysis, demonstrated convincingly the
potential usefulness of noninvasive BCIs for motor restoration,
more clearly than the widely acclaimed and cited animal experi-
ments using implanted microelectrodes. In one preparation,
Pfurtscheller, Neuper, et al. (2003) used the SMR signals to ac-
tivate electric stimulation electrodes attached to the paralyzed
arm and hand muscles in order to reach and grasp objects in a
quadriplegic patient. These data suggest that, with intelligent
prosthetic devices and orthoses, electrical muscle stimulation,
and EMG feedback from the target muscles, noninvasive BCIs
may have promise for highly complex movement reconstruction.
Neuper, Muller, Kubler, Birbaumer, and Pfurtscheller (2003)
demonstrated successfully that the same SMR-based BCI used
for motor control can be used as a communication device in a
paralyzed cerebral palsy patient and that training and measure-
ment may be performed even from laboratories located at long
distances from the patient. However, none of the paralyzed pa-
tients reported in the literature is using the motor BCI in every-
day-life situations as long as voluntary upper face and shoulder
movements can activate an artificial limb. Therefore, in spinal
cord lesioned patients, invasive and noninvasive BCIs (BMIs)
may be useful in the future for the few patients with extremely
high spinal cord lesions only.
Another obstacle for real-life daily use of BCIs regardless of
the type of application is their demand on attention. Whereas
simple motor commands in the intact adult organism are exe-
cuted with a minimum of cognitive resource allocation, the vol-
untary production of brain signals irrespective of the type of
signal needs more and continuous attentional resource mobil-
ization than highly automatized skills because automatization of
brain control is slow and probably never complete (Neumann
et al., 2004). In addition, the noninvasive BCIs allow relatively
undisturbed slowverbal communication, but production ofmove-
ment with brain activity inevitably generates movement-related
artifacts difficult to eliminate online. Particularly in patients with
spasticity and uncontrolled movement episodes, attempts to
produce motor action from EEG signals are often punished by
the presence of these artifacts and cause frustration and decline in
motivation (Birbaumer et al., 2003, 2004; Kubler, Winter, &
Birbaumer, 2003). For these special cases, the implantation of
electrodes may constitute a viable alternative. Whether the elec-
trodes need to penetrate hundreds to thousands of neurons as
some maintain (Nicolelis, 2003) or only small samples of
Breaking the silence 527
Figure 6. Local brain activation measured with fMRI to 60 affective slides with social content. A: Twelve patients with ALS and 14
age-matched healthy controls at two time points. B: Same group after 6 months of disease progression. Activations of healthy
controls subtracted from ALS. Activations in yellow-red indicate more activation in ALS (Lule et al., in press).
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critically important neurons responsible for directional tuning,
for example, is an unresolved question.
Birbaumer, Weber, Buch, Neuper and Cohen (in press) at the
National Institute of Neurological Diseases and Stroke
(NINDS) together with the Tubingen group (Lal et al., 2006)
developed a BCI system for chronic stroke that may solve most
of the problems of noninvasive BCIs devoted to motor restor-
ation and may constitute a sensitive alternative to invasive ap-
proaches. In this preparation, patients with no residual hand
movement are trained with a magnetoencophalography (MEG)-
contolled hand orthosis (Figure 7).
For the first 10 to 20 training sessions in the MEG and after
successful hand opening, closing, and grasping using feedback
and modulation of central SMR magnetic-field oscillations, the
patient is switched to a mobile EEG-SMR-based BCI wearing
the same orthosis. Because brain magnetic fields are not attenu-
ated and distorted on their way from the cortical generators to
the MEG dewar containing the recording SQUIDs, MEG pro-
vides a much larger and more localized SMR response, allowing
control of even single fingers (Braun, Schweizer, Elbert, Birbau-
mer, & Taub, 2000). The head of the patient is fixated in the
dewar and the fingers attached to the orthosis open and close the
hand contingent on SMR increase and decrease. The patient
receives visual and proprioceptive feedback from his/her own
movement and simultaneously watches a screen with an up or
down moving cursor that indicates the amount of SMR present
in the appropriate cortical region 7 s before the self-produced
SMR moves the orthosis attached to the hand. Figure 8 depicts
the SMRmagnetic field localization and training performance of
a patient with long-standing chronic stroke and complete im-
mobility of the affected hand. As a positive side effect, the patient
experienced complete relief of hand spasticity after several train-
ing sessions.
The primary aim of the MEG-BCI training in chronic stroke
is not only restoration of movement but cortical reorganization
and compensatory cerebral activation of nonlesioned brain re-
528 N. Birbaumer
Figure 7. BCI using sensorimotormagnetic field oscillations (CTFMEG
275 channels) for motor restoration of paralyzed hand in chronic stroke.
Top: Feedback curser at the screen indicates amount of SMR present
during 7 s; the goal at the right side of the screen indicates whether the
patient has to increase SMR (lower goal) or decrease it (upper goal). The
orthosis moves the hand proportional to the SMR changed achieved.
Bottom: Experimental situation inMEGwith fingers fixed to the orthosis
opening and closing the hand.
Figure 8. Magnetic field SMR-BCI in a chronic stroke patient. Top:
Magnetic field distribution of 9 Hz magnetic SMR (yellow-brown)
parietal, posterior of lesion, ipsilesional. Bottom: Learning of SMR
control in a chronic stroke patient over 11 sessions.
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gions through voluntary brain-controlled hand movement of the
paralyzed limb and reduction of contralesional hemispheric in-
hibition. Duque et al. (2005), Murase, Duque, Mazzocchio, and
Cohen (2004), and Ward and Cohen (2004) have shown in a
series of transcranial magnetic stimulation (TMS) experiments
that the strong inhibitory effect from the healthy hemisphere on
the lesioned hemisphere may be responsible for the lack of re-
organization and insufficient recovery of the stroke-affected
brain area. Consequently, the MEG-BCI training is targeted to-
ward a ‘‘strenghthening’’ of the ipsilesional brain regions around
the destroyed tissue and ‘‘weakening’’ of the homotypical regions
in the opposite hemisphere. This is achieved by using SMR os-
cillations (from 10 to 20 Hz) as a movement-directing source
originating in the immediate neighborhood of the lesion and
simultaneous interruption of feedback and orthosis control with
contralesional coactivation. Cortical reorganization is measured
before and after training with fMRI of imagined and executed
hand and lip movements as described by Lotze et al. (Lotze,
Braun, Birbaumer, Anders, & Cohen, 2003; Lotze, Grodd, et al.,
1999; Lotze, Montoya, et al., 1999). Whether the training results
in improved hand mobility with or without orthosis is the ques-
tion of the ongoing clinical experiments. Chronic stroke with no
remaining finger mobility is resistant to treatment and shows no
spontaneous recovery; any improvement through BCI training
therefore constitutes a success. Again, invasive implantation of
large quantities of electrodes with the many risks and uncertain-
ties involved may be superfluous or reserved for the few most
difficult cases.
Future Directions: The Metabolic Whole Brain BCI
Weiskopf et al. (2003) for the first time demonstrated convin-
cingly that healthy persons are able to regulate BOLD (blood
oxygen level dependent) responses from circumscribed cortical
and subcortical brain regions using online functional magnetic
resonance imaging (fMRI-BCI). These authors and others
(DeCharms et al., 2005) demonstrated substantial effects of
BOLD-response BCI training on behavior: Pain, emotional
arousal, and memory were investigated and astonishingly strong
effects on the behavioral variables after short training periods
with fMRI-feedback training were shown. This is not surprising,
considering that vascular changes in brain arteries and veins
responsible for metabolic responses such as BOLD and brain
blood flow may allow superior voluntary (operant) control be-
cause of the vascular-motor component of the physiological
target response. Dilation and contraction of vascular changes are
sensed by the brain and regulated by neural structures with
closely coupled autonomic and somatic-motor functions,
allowing access to voluntary control (Dworkin, 1993).
The results presented by Weiskopf et al. (2004), Weiskopf,
Klose, Birbaumer, and Mathiak (2005), and Weiskopf, Schar-
nowski, et al. (2005) constitute the first step in the application of
fMRI-BCI to emotional disorders: fMRI allows anatomically
specific control of subcortical and cortical areas responsible
for the regulation of emotions not as accessible to electro-
physiological methods as EEG and MEG such as amygdala,
limbic insular and cingulate regions, and anterior basal ganglia
(Figure 9).
Clinical application of fMRI-BCI is presently unrealistic and
unlikely, considering the cost and technological difficulties in-
volved in real-time fMRI. It will, at present, remain reserved for
research purposes and experiments intending to demonstrate ef-
fects of learned local blood-flow changes on emotional and mo-
tivational behavior. A clinically more realistic new metabolic
BCI system has been proposed and tested recently by Sitaram
et al. (in press). These investigators usednear-infrared spectroscopy
(NIRS) andmeasured, with optical recording devices, changes in
cortical oxygenation and deoxygenation. Using the reflection of
light in living tissues with high circulation density such as the
brain, NIRS is completely noninvasive (Coyle et al., 2004).
NIRS devices are also relatively inexpensive (price equivalent to
that of a multichannel EEG) and commercially available. An-
other virtue of NIRS is portability, allowing, for example, the
training of young children. Sitaram et al. (in press) demonstrated
online operant control of sensorimotor brain areas in five healthy
subjects and spelling of letters with NIRS-BCI with an accuracy
of 70%–95% after only two training sessions and with informa-
tion transfer speed comparable to EEG-BCI.
Epilog
Brain–computer interfaces or brain–machine interfaces are in-
tended to translate ‘‘thought into action’’ with brain activity
only. The research devoted to this goal has raised many fascin-
ating questions about brain–behavior relationships without
achieving its ultimate practical goals: communication with the
completely paralyzed and restoration of movement in paralysis.
But the reformulation of the problem of how brain cells and their
output create observable behavior applied to an existential prob-
lem of human suffering will focus the questions we ask in cog-
nitive neuroscience and psychophysiology. BCI research
stimulates long-held hope and expectation of thought and emo-
tion detection and translation from brain states. And true to the
old Yiddish saying, ‘‘Fur lojter hofenung wer ich noch mes-
chugge’’ [I am crazy with hope].
Breaking the silence 529
Figure 9. FMRI-BCI, experimental setup. Subject (brain in center)
watches screen with yellow line (left) representing BOLD response.
Required increase of BOLD is indicated by green bar, decrease by blue
bar. Signals are processed in a 3 T Siemens Trio Scanner (right) online
using Brain Voyager (below right). Below left: Subject receives feedback
of the BOLD difference between two areas of interest (from Weiskopf
et al. 2004; Weiskopf, Veit, et al., 2005).
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REFERENCES
Albert, S., Rabkin, J., Del Bene,M., Tider,M., &Mitsumoto, H. (2005).Wish to die in end-stage ALS. Neurology, 65, 68–74.
Bandura, A. (1969). Social learning of moral judgements. Journal ofPersonality and Social Psychology, 11, 275–279.
Berger, H. (1929). Ueber das Elektrenkephalogramm des Menschen.Archiv fur Psychiatrie und Nervenkrankheiten, 87, 527–570.
Birbaumer, N. (1977). Operant enhancement of EEG-theta activity: As-piration and reality. In J. Beatty & H. Legewie (Eds.), Biofeedbackand Behavior (pp. 135–146). New York: Plenum Press.
Birbaumer, N. (1999). Slow cortical potentials: Plasticity, operant con-trol, and behavioral effects. The Neuroscientist, 5, 74–78.
Birbaumer, N., Elbert, T., Canavan, A., & Rockstroh, B. (1990). Slowpotentials of the cerebral cortex and behavior. Physiological Reviews,70, 1–41.
Birbaumer, N., Elbert, T., Rockstroh, B., & Lutzenberger, W. (1986).Biofeedback of slow cortical potentials in attentional disorders. InW. C. McCallum, R. Zappoli, & F. Denoth (Eds.), Cerebral psycho-physiology: Studies in event-related potentials (pp. 440–442). Amster-dam: Elsevier.
Birbaumer, N., Flor, H., Cevey, B., Dworkin, B., &Miller, N. E. (1994).Behavioral treatment of scoliosis and kyphosis. Journal of Psycho-somatic Research, 6, 623–628.
Birbaumer, N., Flor, H., Lutzenberger, W., & Elbert, T. (1995). Chaosand order in the human brain. In G. Karmos & M. Molnar (Eds.),Perspectives of event-related potentials research (EEG Suppl. 44)(pp. 450–459). Amsterdam: Elsevier.
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchou-bey, B., Kubler, A., et al. (1999). A spelling device for the paralysed.Nature, 398, 297–298.
Birbaumer, N., Hinterberger, T., Kubler, A., & Neumann, N. (2003).The thought-translation-device (TTD): Neurobehavioral mecha-nisms and clinical outcome. IEEE Transactions on Neural Systemsand Rehabilitation Engineering, 11, 120–123.
Birbaumer, N., & Kimmel, H. (Eds.). (1979). Biofeedback and self-regu-lation. Hillsdale, NJ: Erlbaum.
Birbaumer, N., Roberts, L., Lutzenberger, W., Rockstroh, B., & Elbert,T. (1992). Area-specific self-regulation of slow cortical potentials onthe sagittal midline and its effects on behavior. Electroencephalog-raphy and Clinical Neurophysiology, 84, 353–361.
Birbaumer, N., Strehl, U., & Hinterberger, T. (2004). Brain–computerinterfaces for verbal communication. In G. Dhillon & K. Horch(Eds.),Neuroprosthetics: Theory and practice (pp. 1146–1157). Hack-ensack, NJ: World Scientific Publishing.
Birbaumer, N., Weber, C., Buch, E., Neuper, C., & Cohen, L. (in press).Oscillatory brain-computer-interfaces (BCI) for communication andin chronic stroke. Progress in Brain Research.
Braitenberg, V., & Schuz, A. (1991). Anatomy of the cortex. Berlin,Heidelberg, New York: Springer.
Braun, C., Schweizer, R., Elbert, T., Birbaumer, N., & Taub, E. (2000).Differential activation in somatosensory cortex for different discrim-ination tasks. The Journal of Neuroscience, 20, 446–450.
Breitbart, W., Rosenfeld, B., & Penin, H. (2000). Depression, hopeless-ness, and desire for hastened death in terminally ill patients with can-cer. JAMA, 284, 2907–2911.
Brunner, C., Graimann, B., Huggins, J. E., Levine, S. P., & Pfurtscheller,G. (2005). Phase relationships between different subdural electroderecordings in man. Neuroscience Letters, 375, 69–74.
Carmena, J. M., Lebedev, M. A., Crist, R. E., O’Doherty, J. E., Sant-ucci, D. M., Dimitrov, D. F., et al. (2003). Learning to control abrain-machine interface for reaching and grasping by primates. PLoSBiology, 1, E42.
Chapin, J. K., Moxon, K. A., Markowitz, R. S., & Nicolelis, M. A. L.(1999). Real-time control of a robot arm using simultaneously re-corded neurons in themotor cortex.Nature Neuroscience, 2, 664–670.
Coyle, S., Ward, T., Markham, C., & McDarby, G. (2004). On thesuitability of near infrared (NIR) systems for next-generation brain–computer interfaces. Physiological Measurement, 25, 815–822.
Cuthbert, B., Kristeller, J., Simons, R., Hodes, R., & Lang, P. J. (1981).Strategies of arousal control: Biofeedback, meditation, and motiv-ation. Journal of Experimental Psychology. General, 110, 518–546.
DeCharms, R. C., Maeda, F., Glover, G. H., Ludlow, D., Pauly, J. M.,Soneji, D., et al. (2005). Control over brain activation and pain
learned by using real-time functional MRI. Proceedings of the Na-tional Academy of Sciences, USA, 102, 18626–18631.
Donoghue, J. P. (2002). Connecting cortex tomachines: Recent advancesin brain interfaces. Nature Neuroscience, 5, 1085–1088.
Duque, J., Hummel, F., Celnik, P., Murase, N., Mazzocchio, R., &Cohen, L. G. (2005). Transcallosal inhibition in chronic subcorticalstroke. Neuroimage, 28, 940–946.
Dworkin, B. R. (1993). Learning and physiological regulation. Chicago:University of Chicago Press.
Dworkin, B. R., & Miller, N. E. (1986). Failure to replicate viscerallearning in the acute curarized rat preparation. Behavioral Neurosci-ence, 100, 299–314.
Dworkin, B., Miller, N. E., Dworkin, S., Birbaumer, N., Brines, M.,Jonas, S., et al. (1985). Behavioral method for the treatment of idio-pathic scoliosis. Proceedings of the National Academy of Science,USA, 82, 2493–2497.
Elbert, T., Rockstroh, B., Lutzenberger, W., & Birbaumer, N. (Eds.).(1984). Self-regulation of the brain and behavior. New York: Springer.
Engel, B. T. (1981). Clinical biofeedback: A behavioral analysis. Neu-roscience and Biobehavioral Reviews, 5, 397–400.
Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head:Toward a mental prosthesis utilizing event-related brain potentials.Electroencephalography and Clinical Neurophysiology, 70, 510–523.
Fetz, E. E. (1969). Operant conditioning of cortical unit activity. Science,163, 955–958.
Flor, H., & Birbaumer, N. (1993). Comparison of the efficacy of EMGbiofeedback, cognitive behavior therapy, and conservative medicalinterventions in the treatment of chronic musculoskeletal pain. Jour-nal of Consulting & Clinical Psychology, 61, 653–658.
Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of thebasis of social cognition. Trends in Cognitive Sciences, 8, 396–403.
Gastaut, H. (1952). Electrocorticographic study of the reactivity of ro-landic rhythm. Review Neurologique (Paris), 87, 176–182.
Gastaut, H., Terzian, H., & Gastaut, Y. (1952). Study of a little elec-troencephalographic activity: Rolandic arched rhythm. MarseilleMedical, 89, 296–310.
Georgopoulos, A. P., Schwartz, A. B., &Kettner, R. E. (1986).Neuronalpopulation coding of movement direction. Science, 233, 1416–1419.
Graimann, B., Huggins, J. E., Levine, S. P., & Pfurtscheller, G. (2004).Toward a direct brain interface based on human subdural recordingsand wavelet-packet analysis. IEEE Transactions on Biomedical Engi-neering, 51, 954–962.
Haggard, P., Clark, S., & Kalogeras, J. (2002). Voluntary action andconscious awareness. Nature Neuroscience, 5, 382–385.
Hill, N. J., Lal, T. N., Schroder, M., Hinterberger, T., Widman, G.,Elger, C. E., et al. (in press) Classifying event-related desynchroniza-tion in EEG, ECoG and MEG signals. In G. Dornhege, et al. (eds.),Brain Computer Interfaces (BCI). MIT Press.
Hinterberger, T., Birbaumer, N., & Flor, H. (2005). Assessment of cog-nitive function and communication ability in a completely locked-inpatient. Neurology, 64, 1307–1308.
Hinterberger, T., Kubler, A., Kaiser, J., Neumann, N., & Birbaumer, N.(2003). A brain–computer interface (BCI) for the locked-in: Com-parison of different EEG classifications for the thought translationdevice. Clinical Neurophysiology, 114, 416–425.
Hinterberger, T., Veit, R., Strehl, U., Trevorrow, T., Erb, M., Kotchou-bey, B., et al. (2003). Brain areas activated in fMRI during self regu-lation of slow cortical potentials (SCPs). Experimental BrainResearch, 152, 113–122.
Hinterberger, T., Veit, R., Wilhelm, B., Weiskopf, N., Vatine, J.-J., &Birbaumer, N. (2005). Neuronal mechanisms underlying control of abrain–computer interface. European Journal of Neuroscience, 21,3169–3181.
Hinterberger, T., Weiskopf, N., Veit, R., Wilhelm, B., Betta, E., & Bi-rbaumer, N. (2004). An EEG-driven brain–computer interface com-bined with functional magnetic resonance imaging (fMRI). IEEETransactions on Biomedical Engineering, 51, 971–974.
Hochberg, L. R., Mukand, J. A., Polykoff, G. I., Friehs, G. M., &Donoghue, J. P. (2005). Braingate neuromotor prosthesis: Natureand use of neural control signals. ProgramNo. 520.17. 2005 AbstractViewer/Itinerary Planner. Washington, DC: Society for Neuroscience.Available at: http://sfn.scholarone.com/itin2005/.
530 N. Birbaumer
![Page 15: Niels Birbaumer- Breaking the silence: Brain–computer interfaces (BCI) for communication and motor control](https://reader036.fdocuments.us/reader036/viewer/2022062405/55720c1d497959fc0b8c3955/html5/thumbnails/15.jpg)
Holland, J., & Skinner, B. F. (1961). The analysis of behavior. NewYork:MacGraw-Hill.
Holzl, R., & Whitehead, W. (Eds.). (1983). Psychophysiology of the gas-trointestinal tract. New York: Plenum Press.
James,W. (1890).The principles of psychology. NewYork: Holt. Reprint:Cambridge, MA: Harvard University Press, 1983.
Kaiser, J., Lutzenberger, W., Preissl, H., Ackermann, H., & Birbaumer,N. (2000). Right-hemisphere dominance for the processing of sound–source lateralization. Journal of Neuroscience, 20, 6631–6639.
Kamiya, J. (Ed.). (1971). Biofeedback and self-control: An Aldine readeron the regulation of bodily processes and consciousness. Chicago: Al-dine.
Kennedy, P. R., & Adams, K. D. (2003). A decision tree for brain-computer interface devices. IEEETransactions on Neural Systems andRehabilitation Engineering, 11, 148–150.
Kennedy, P. R., Bakay, R. A. E., Moore, M. M., Adams, K., & Goldw-aithe, J. (2000). Direct control of a computer from the human centralnervous system. IEEE Transactions on Rehabilitation Engineering, 8,198–202.
Kennedy, P. R., Kirby, M. T., Moore, M. M., King, B., & Mallory, A.(2004). Computer control using human intracortical local field po-tentials. IEEE Transactions on Neural Systems and Rehabilitation En-gineering, 12, 339–344.
Kotchoubey, B., Lang, S., Bostanov, V., & Birbaumer, N. (2002). Isthere a mind? Electrophysiology of the unconscious. News in Physio-logical Sciences, 17, 38–42.
Kotchoubey, B., Lang, S., Mezger, G., Schmalohr, D., Schneck, M.,Semmler, A., Bostanov, B., & Birbaumer, N. (2005). Informationprocessing in severe disorders of consciousness: Vegetative stateand minimally conscious state. Clinical Neurophysiology, 116,2441–2453.
Kotchoubey, B., Strehl, U., Uhlmann C, Holzapfel, S, Konig, M.,Froscher, W., et al. (2001). Modification of slow cortical potentials inpatients with refractory epilepsy: A controlled outcome study. Epi-lepsia, 42, 406–416.
Kubler, A., Kotchoubey, B., Kaiser, J., Wolpaw, J., & Birbaumer, N.(2001). Brain-computer communication: Unlocking the locked-in.Psychological Bulletin, 127, 358–375.
Kubler, A., Neumann, N., Kaiser, J., Kotchoubey, B., Hinterberger, T.,& Birbaumer, N. (2001). Brain–computer communication: Self-regu-lation of slow cortical potentials for verbal communication. Archivesof Physical Medicine and Rehabilitation, 82, 1533–1539.
Kubler, A., Nijboer, F., Mellinger, J., Vaughan, T. M., Pawelzik, H.,Schalk, G., et al. (2005). Patients with ALS can use sensorimotorrhythms to operate a brain-computer interface. Neurology, 64, 1775–1777.
Kubler, A., Winter, S., & Birbaumer, N. (2003). The thought translationdevice: Slow cortical potential biofeedback for verbal communicationin paralyzed patients. In M. Schwartz & F. Andrasik (Eds.), Bio-feedback: A practitioner’s guide (3rd ed, pp. 471–481). New York:Guilford Press.
Kubler, A., Winter, S., Ludolph, A. C., Hautzinger, M., & Birbaumer,N. (2005). Severity of depressive symptoms and quality of life inpatients with amyotrophic lateral sclerosis. Neurorehabilitation andNeural Repair, 19, 182–193.
Lal, T. N., Schroder, M., Hill, J. N., Hinterberger, T., Mellinger, J.,Rosenstiel, W., et al. (2006). A brain computer interface with on-linefeedback based on magnetoencephalography. Manuscript submittedfor publication.
Lal, T. N., Schroder, M., Hill, J. N., Rosenstiel, W., Elger, C. E.,Scholkopf, B., et al. (2005). Methods towards invasive human braincomputer interfaces. In L. K. Soul, Y. Weiss, & L. Bottou (Eds.),Advances in Neural Information Processing Systems (vol. 17, pp. 737–744). Cambridge, MA: MIT Press.
Lal, T. N., Schroder, M., Hinterberger, T., Weston, J., Bogdan, M.,Birbaumer, N., et al. (2004). Support vector channel selection in BCI.IEEE Transactions on Biomedical Engineering, 51, 1003–1010.
Lang, P., Bradley, M., & Cuthbert, B. (1999). International AffectivePicture System. Gainesville, Fl: The Center for Research in Psycho-physiology, University of Florida.
Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., & Moran,D.W. (2004). A brain-computer interface using electrocorticographicsignals in humans. Journal of Neural Engineering, 1, 63–71.
Libet, B., Gleason, C. A., Wright, E. W., & Pearl, D. K. (1983). Time ofconscious intention to act in relation to onset of cerebral activity
(readiness-potential). The unconscious initiation of a freely voluntaryact. Brain, 106, 623–642.
Lotze,M., Braun, C., Birbaumer, N., Anders, S., & Cohen, L. G. (2003).Motor learning elicited by voluntary drive. Brain, 126, 866–872.
Lotze, M., Grodd, W., Birbaumer, N., Erb, M., Huse, E., & Flor, H.(1999). Does use of a myoelectric prosthesis prevent cortical reorgan-ization and phantom limb pain? Nature Neuroscience, 2, 501–502.
Lotze, M., Montoya, P., Erb, M., Hulsmann, E., Flor, H., Klose, U., etal. (1999). Activation of cortical and cerebellar motor areas duringexecuted and imagined hand movements: An fMRI study. Journal ofCognitive Neuroscience, 11, 491–501.
Lule, D., Gramm,R., Kurt, A., Kassubek, J., Diekmann, V., Birbaumer,N., et al. (in press). The functional anatomy of motor execution andimagery in amyotrophic lateral sclerosis. Journal of Neurology.
Lule, D., Kurt, A., Jurgens, R., Kassubek, J., Diekmann, V., Kraft, E.,et al. (2005). Emotional responding inAmyotrophic Lateral Sclerosis.Journal of Neurology, 252, 1517–1524.
Lutzenberger,W., Birbaumer, N., Elbert, T., Rockstroh, B., Bippus, W.,& Breidt, R. (1980). Self-regulation of slow cortical potentials innormal subjects and in patients with frontal lobe lesions. In H. H.Kornhuber & L. Deecke (Eds.), Motivation, motor and sensory pro-cesses of the brain: Electrical potentials, behavior and clinical use (pp.427–430). Amsterdam: Elsevier.
Lutzenberger, W., Roberts, L. E., & Birbaumer, N. (1993). Memoryperformance and area-specific self-regulation of slow cortical poten-tials: Dual-task interference. International Journal of Psychophysio-logy, 15, 217–226.
McGrady, A., Olson, P., &Kroon, J. (1995). Biobehavioral treatment ofessential hypertension. In M. Schwartz (Ed.), Biofeedback (2nd ed,pp. 445–467). New York: Guilford.
Miller, N. (1969). Learning of visceral and glandular responses. Science,163, 434–445.
Mulholland, T., & Evans, C. R. (1966). Oculomotor functions and theelectroencephalographic alpha activation cycle. Nature, 211, 1278–1279.
Murase, N., Duque, J., Mazzocchio, R., & Cohen, L. G. (2004). Influ-ence of interhemispheric interactions on motor function in chronicstroke. Annals of Neurology, 55, 400–409.
Musallam, S., Corneil, B. D., Greger, B., Scherberger, H., & Andersen,R. A. (2004). Cognitive control signals for neural prosthetics. Science,305, 258–262.
Neumann, N., Hinterberger, T., Kaiser, J., Leins, U., Birbaumer, N., &Kubler, A. (2004). Automatic processing of self-regulation of slowcortical potentials: Evidence from brain-computer communication inparalysed patients. Clinical Neurophysiology, 115, 628–635.
Neuper, C., Muller, G. R., Kubler, A., Birbaumer, N., & Pfurtscheller,G. (2003). Clinical application of an EEG-based brain-computerinterface: A case study in a patient with severe motor impairment.Clinical Neurophysiology, 114, 399–409.
Nicolelis, M. A. L. (2001). Actions from thoughts. Nature, 409,403–407.
Nicolelis, M. A. (2003). Brain-machine interfaces to restore motor func-tion and probe neural circuits. Nature Reviews Neuroscience, 4, 417–422.
Nicolelis, M. A. L., Birbaumer, N., & Mueller, K.-L. (2004). Brain-computer interfaces. Guest editorial. IEEE Transactions on Biomed-ical Engineering, 51, 877–880.
Norris, F. H. (1992). Amyotrophic lateral sclerosis: The clinical disorder.In R. A. Smith (Ed),Handbook of Amyottrophic Lateral Sclerosis (pp.3–38). New York: Marcel Dekker.
Perelmouter, J., & Birbaumer, N. (2000). A binary spelling interface withrandom errors. IEEE Transactions on Rehabilitation Engineering, 8,227–232.
Pfurtscheller, G., Mueller, G. R., Pfurtscheller, J., Gerner, H. J., &Rupp, R. (2003). Thought’’-control of functional electric stimulationto restore hand grasp in a patient with tetraplegia. Neuroscience Let-ters, 382, 169–174.
Pfurtscheller, G., Neuper, C., & Birbaumer, N. (2005). Human brain–computer interface (BCI). In A. Riehle & E. Vaadia (Eds.), Motorcortex in voluntary movements. A distributed system for distributedfunctions (pp. 367–401). Boca Raton, FL: CRC Press.
Pfurtscheller, B., Neuper, C., Muller, G. R., Obermaier, B., Krausz, G.,Schlogl, A., et al. (2003). Graz-BCI: State of the art and clinicalapplications. IEEETransactions on Neural Systems and RehabilitationEngineering, 11, 177–180.
Breaking the silence 531
![Page 16: Niels Birbaumer- Breaking the silence: Brain–computer interfaces (BCI) for communication and motor control](https://reader036.fdocuments.us/reader036/viewer/2022062405/55720c1d497959fc0b8c3955/html5/thumbnails/16.jpg)
Piccione, F., Giorgi, F., Tonin, P., Priftis, K., Giore, S., Silvani, S., et al.(2006). P300-based brain computer interface: Reliability and per-formance in healthy and paralysed participants. Clinical Neurophysi-ology, 117, 531–537.
Quill, T. E. (2005). ALS, depression, and desire for hastened death:(How) are they related? Neurology, 65, 1.
Riehle, A., & Vaadia, E. (Eds.). (2005). Motor cortex in voluntary move-ments. A distributed system for distributed functions. Boca Raton:CRC Press.
Rockstroh, B., Elbert, T., Birbaumer, N., & Lutzenberger, W. (1989).Slow brain potentials and behavior (2nd ed). Baltimore, MD: Urban &Schwarzenberg.
Rockstroh, B., Elbert, T., Birbaumer, N., Wolf, P., Duchting-Roth, A.,Reker, M., et al. (1993). Cortical self-regulation in patients with epi-lepsies. Epilepsy Research, 14, 63–72.
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wol-paw, J. R. (2004). BCI2000: A general-purpose brain–computerinterface (BCI) system. IEEETransactions on Biomedical Engineering,51, 1034–1043.
Schneider, F., Rockstroh, B., Heimann, H., Lutzenberger, W., Mattes,R., Elbert, T., et al. (1992). Self-regulation of slow cortical potentialsin psychiatric patients: Schizophrenia.Biofeedback & Self-Regulation,17, 277–292.
Schroder, M., Lal, T. N., Hinterberger, T., Bodgan, M., Hill, N. J.,Birbaumer, N., et al. (in press). Robust EEG channel selection acrosssubjects for brain-computer interfaces. EURASIP Journal on AppliedSignal Processing.
Schwartz, A. B., Taylor, D. M., & Tillery, S. I. H. (2001). Extractionalgorithms for cortical control of arm prosthetics. Current Opinion inNeurobiology, 11, 701–708.
Seligman, M. E. P. (1975). Helplessness. Freeman, San Francisco.Sellers, E. W., & Donchin, E. A. (2006). A P300 based brain-computer
interface: Initial tests by ALS patients. Clinical Neurophysiology, 117,538–548.
Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., &Donoghue, J. P. (2002). Instant neural control of a movement signal.Nature, 416, 141–142.
Singer, W., & Gray, Ch. (1994/1995). Visual feature integration and thetemporal correlation hypothesis. Annual Review of Neuroscience, 18,555–586.
Sitaram, R., Guan, C., Zhang, H., Thulasidas, M., Hoshi, Y., & Bi-rbaumer, N. (in press). A brain–computer interface using multi-channel near infrared spectroscopy. IEEE Transactions on BiomedicalEngineering.
Skinner, F. (1953). Science and Human Behavior. New York: Macmillan.Sterman, M. B. (1977). Sensorimotor EEG operant conditioning: Ex-
perimental and clinical effects. The Pavlovian Journal of BiologicalScience, 12, 63–92.
Sterman, M. B. (1981). EEG biofeedback: Physiological behavior modi-fication. Neuroscience and Biobehavioral Reviews, 5, 405–412.
Sterman, M. B., & Clemente, C. D. (1962a). Forebrain inhibitory mech-anisms: Cortical synchronization induced by basal forebrain stimu-lation. Experimental Neurology, 6, 91–102.
Sterman, M. B., & Clemente, C. D. (1962b). Forebrain inhibitory mech-anisms: Sleep patterns induced by basal forebrain stimulation in thebehaving cat. Experimental Neurology, 6, 103–117.
Sterman,M. B., & Friar, L. (1972). Suppression of seizures in an epilepticfollowing sensorimotor EEG feedback training. Electroencephalog-raphy and Clinical Neurophysiology, 33, 89–95.
Strehl, U., Leins, U., Goth, G., Klinger, C., & Birbaumer, N. (in press).Physiological regulation of slow cortical potentialsFA new treat-ment for children with ADHD. Pediatrics.
Taylor, D. M., Tillery, S. I., & Schwartz, A. B. (2002). Direct corticalcontrol of 3D neuroprosthetic devices. Science, 296, 1829–1832.
Tregoubov, M., & Birbaumer, N. (2005). On the building of binaryspelling interfaces for augmentative communication. IEEE Transac-tions on Biomedical Engineering, 52, 300–305.
Walter, W. G. (1964). The contingent negative variation. An electricalsign of significance of association in the human brain. Science, 146,434.
Ward, N. S., & Cohen, L. G. (2004). Mechanisms underlying recovery ofmotor function after stroke. Archives of Neurology, 61, 1844–1848.
Weiskopf, N., Klose, U., Birbaumer, N., & Mathiak, K. (2005). Single-shot compensation of image distortions and BOLD contrast optimi-zation using multi-echo EPI for real-time fMRI. NeuroImage, 24,1068–1079.
Weiskopf, N., Mathiak, K., Bock, S. W., Scharnowski, F., Veit, R.,Grodd, W., et al. (2004). Principles of a brain-computer-interface(BCI) based on real-time functional magnetic resonance imaging(fMRI). IEEE Transactions on Biomedical Engineering, 51, 966–970.
Weiskopf, N., Scharnowsi, F., Veit, R., Goebel, R., Birbaumer, N., &Mathiak, K. (2005). Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). Journal ofPhysiology, Paris, 98, 357–373.
Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R., etal. (2003). Physiological self-regulation of regional brain activityusing real-time functional magnetic resonance imaging (fMRI):Methodology and exemplary data. NeuroImage, 19, 577–586.
Wilhelm, B., Jordan, M., & Birbaumer, N. (2006). Communication inlocked-in syndrome: Effects of imagery on salivary pH. Neurology,67, 534–535.
Wolpaw, J. R., Birbaumer, N., McFarland, D., Pfurtscheller, G., &Vaughan, T. (2002). Brain-computer interfaces for communicationand control. Clinical Neurophysiology, 113, 767–791.
Wolpaw, J. R., &McFarland, D. J. (2004). Control of a two-dimensionalmovement signal by a noninvasive brain–computer interface in hu-mans. Proceedings of the National Academy of Sciences, USA, 101,17849–17854.
(Received March 30, 2006; Accepted July 11, 2006)
532 N. Birbaumer