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Brain-Computer Interface Seminar’09

1.INTRODUCTION

What is a Brain-Computer Interface?

A brain-computer interface uses electrophysiological signals to

control remote devices. Most current BCIs are not invasive. They consist

of electrodes applied to the scalp of an individual or worn in an electrode

cap such as the one shown in 1-1 (Left). These electrodes pick up the

brain’s electrical activity (at the microvolt level) and carry it into

amplifiers such as the ones shown in 1-1 (Right). These amplifiers amplify

the signal approximately ten thousand times and then pass the signal via

an analog to digital converter to a computer for processing. The computer

processes the EEG signal and uses it in order to accomplish tasks such as

communication and environmental control. BCIs are slow in comparison

with normal human actions, because of the complexity and noisiness of

the signals used, as well as the time necessary to complete recognition

and signal processing.

Figure 1: an example to show how the electrodes are placed

Dept. of Computer Science & Engg: 1 M E S C E Kuttipuram


The phrase brain-computer interface (BCI) when taken literally means to

interface an individual’s electrophysiological signals with a computer. A

true BCI only uses signals from the brain and as such must treat eye and

muscle movements as artifacts or noise. On the other hand, a system that

uses eye, muscle, or other body potentials mixed with EEG signals, is a

brain-body actuated system.

Figure 2 : Scheme of an EEG-based Brain Computer Interface with on-line

feedback. The EEG is recorded from the head surface, signal processing

techniques are used to extract features. These features are classified, the output

is displayed on a computer screen. This feedback should help the subject to

control its EEG patterns.

The BCI system uses oscillatory electroencephalogram (EEG)

signals, recorded during specific mental activity, as input and provides a



control option by its output. The obtained output signals are presently

evaluated for different purposes, such as cursor control, selection of

letters or words, or control of prosthesis. People who are paralyzed or

have other severe movement disorders need alternative methods for

communication and control. Currently available augmentative

communication methods require some muscle control. Whether they use

one muscle group to supply the function normally provided by another

(e.g., use extraocular muscles to drive a speech synthesizer) .Thus, they

may not be useful for those who are totally paralyzed (e.g., by

amyotrophic lateral sclerosis (ALS) or brainstem stroke) or have other

severe motor disabilities. These individuals need an alternative

communication channel that does not depend on muscle control. The

current and the most important application of a BCI is the restoration of

communication channel for patients with locked-in-syndrome.

2. BRAIN-COMPUTER INTERFACE ARCHITECTURE



Figure 3: architecture

The processing unit is subdivided into a preprocessing unit,

responsible for artefact detection, and a feature extraction and

recognition unit that identifies the command sent by the user to the BCI.

The output subsystem generates an action associated to this command.

This action constitutes a feedback to the user who can modulate her

mental activity so as to produce those EEG patterns that make the BCI

accomplish her intents.



2.1. THE PARTS OF A BCI

2.1.1. SIGNAL ACQUISITION

In BCIs, the input is EEG recorded from the scalp or the

surface of the brain or neuronal activity recorded within the brain.

Electrophysiological BCis can be categorized by whether they use non-

invasive (e.g. EEG) or invasive (e.g. intracortical) methodology. They can

also be categorized y whether they use evoked or spontaneous inputs.

Evoked inputs (e.g. EEG produced by flashing letters) result from

stereotyped sensory stimulation provided by the BCI. Spontaneous inputs

(e.g. EEG rhythms over sensorimotor cortex) do not depend for their

generation on such stimulation. There s presumably, no reason why a BCI

could not combine non-invasive and invasive methods or evoked and

spontaneous inputs. In the signal-acquisition part of BCI operation, the

chosen input is acquired by the recording electrodes, amplified, and

digitized.

Most current BCIs use electrophysiological signal features that

represent brain events that are reasonably well-defined anatomically and

physiologically. These include rhythms reflecting oscillations in particular

neuronal circuits (e.g. mu or beta rhythms from sensorimotor cortex),

potentials evoked from particular brain regions by particular stimuli (e.g.

VEPs or P300s), or action potentials produced by particular cortical

neurons. A few are exploring signal features, such as autoregressive

parameters, that bear complex and uncertain relationships to underlying

brain events. The special characteristics and capacities of each signal

feature will largely determine the extent and nature of its usefulness.

SCPs are, as their name suggest, slow. They develop over

300ms to several seconds. Thus, if an SCP based BCI is to exceed a bit

rate of one every 1-2s, users will need to produce more than two SCP



levels at one location, and/or control SCPs at several locations

independently. Initial studies suggest that such control may be possible.

While mu and beta rhythms have characteristics frequencies of 8-12 and

18-26 Hz , respectively, change in mu or beta rhythm amplitude appears

to have a latency of about .5s. On other hand, users are certainly able to

provide more than two amplitude levels, and can achieve independent

control of different rhythms.



Projecting from results to date, a mu/beta rhythm BCI might

select among 4 or more choices every 2-3s. While the possibility for

distinguishing more two amplitude ranges from VEPs or P 300 potentials

has not been explored, these potentials can be evoked in partially

overlapping series of trials, so that selection rate can be increased.

Alternatively or in addition, selection rate might be increased if users

could learnt of control shorter-Latency evoked potential. He firing rates of

individual cortical neurons, if they prove to be independently controllable

in the absence of the concurrent motor outputs and sensor input that

normally accompany and reflect their activity, might support quite high

information transfer rates.

The key determinant of a signal features values is its co

relation with the user’s item, that is, the level of voluntary control the user

achieves over it. Users are likely to differ in the signal features they can

best control. In 3 users nearly locked in by ALS, researchers found that

one used a positive SCP, another a relatively fast negative positive SCP

shift, and a third a P300. Once develop ed, these strategies were

extremely resistant to change. Particularly early in training, BCI systems

should be able to identify, accommodate, and encourage the signal

features best suited to each user. User training may be the most

important and least understood factor affecting the BCI capabilities of

different signal features.

Up to now, researchers have usually assumed that basic

learning principles apply. However, BCI signal features are not normal or

nature brain output channels. They are artificial output channels created

by BCI system. It is not yet clear to what extent these new artificial

outputs will observe known conditioning principles. For example, mu

rhythms and other features generated in sensorimotor cortex, which is

directly involved in motor output, may prove more useful than alpha



rhythms generated in visual or auditory cortex, which is strongly

influenced by sensory input.

The success of neuronally based BCI methods will presumably

also vary from area to area. Initial efforts have focused on neurons in

motor cortex. While this focus is logical, other cortical areas and even sub

cortical areas warrant exploration. For example, in a user paralyzed by a

peripheral nerve or muscle disorder, the activity of spinal cord

motoneurons controlling specific muscles, detected by implanted

electrodes, might prove most useful for communication and control.



2 .1.2 FEATURE EXTRACTION

The performance of a BCI, like that of other communication

system, depends on its signal-to-noise ration. The goal is to recognize and

execute the user’s intent, and the signals are those aspects of the

recorded electrophysiological activity that correlate with and thereby

reveal that intent. The user’s task is to maximize this correlation; and the

system’s first task is to measure the signal features accurately, i.e. to

maximize the signal-to-noise ratio.

When the features are mu rhythms from sensorimotor cortex,

noise includes visual alpha rhythms, and when the features are the firing

rates of specific neurons, noise includes activity of other neurons. Of

particular importance for EEG- based BCIs is the detection and/or

elimination of non-CNS activity, such as EMG from cranial or facial

muscles and EOG feature extraction methods can greatly affect signal- to

noise ratio. Good methods enhance the signal and reduce CNS and non-

CNS noise. This is most important and difficult when the noise is similar to

the signal. For example, EOG is of more concern than EMG for a BCI that

uses SCPs as signal feature, because EOG and SCPs have overlapping

frequency ranges; and for the same reason EMG is of more concern then

EOG for BCIs that use beta rhythms.

A variety of option for improving BCI signal-to-noise ratios are

under study. These include spatial and temporal filtering techniques,

signal averaging, and single-trial recognition methods. Much work up to

now has focused on showing by offline data analyses that a given method

will work. Careful comparisons of alternative methods are also essential. A

statistical measure useful in such comparisons is r, the proportion of the

total variance in the signal feature that is accounted for by the user’s

intent. Alternative feature extraction methods can be compared in terms

of r2. At the same time, of course, it is essential to insure that a high r2 is



not being achieved by non CNS activity such as EMG. Finally, any method

must ultimately be shown to be useful for actual online operation.

Spatial filters derive signal features by combining data from

two or more locations so as to focus on activity with a particular spatial

distribution. The simplest spatial filter is the bipolar derivation, which

derives the first spatial derivative and thereby enhances differences in the

voltage gradient in one direction.



The Laplacian derivation is the second derivative of the

instantaneous spatial voltage distribution, and thereby emphasizes

activity in radial sources immediately below the recording location. It can

be computed by combining the voltage at the location with the voltages of

surrounding electrodes. As the distance to the surrounding electrodes

decreases, the Laplacian becomes ore sensitive to voltage sources with

higher spatial frequencies (i.e. more localized sources) and less sensitive

to those with lower spatial frequencies (i.e. more broadly distributed

sources).

The choice of a spatial filter can markedly affect the signal-to-

noise ratio of a BCI that uses mu and beta rhythms. On the other hand, a

spatial filter best suited for mu and beta rhythms, which are relatively

localized, would probably not be the best choice for measurement of SCPs

OR P300s, which are more broadly distributed over the scalp. Laplacian

and common average reference spatial filters apply a fixed set of weights

to a linear combination of channels (i.e. electrode locations). Both use

weights that sum to zero so that the result is a difference and the spatial

filter has high-pass characteristics. Other spatial filters are available.

Principal components, independent components, and common

spatial patterns analyses are alternative methods for deriving weights for

a linear combination of channels. In these methods, the weights are

determined by the data. Principal components analyses, which produce

orthogonal components, may not be appropriate for separation o signal

features from overlapping sources. Independent components analysis can,

in principle, distinguish between mu rhythms from such sources. These

methods have yet to be compared to simpler spatial filters like the

Laplacian, in which the channel weights are data independent.

Appropriate temporal filtering can also enhance signal to noise ratios.



Oscillatory signals like the mu rhythms can be measured by

the integrated output of a band-pass filter or by the amplitude in specific

spectral bands of Fourier or autoregressive analysis. Because BCis must

provide relatively rapid user feedback and because signals may change

rapidly, frequency analysis methods (e.g. band-pass filters or

autoregressive methods) that need only relatively short time segments

may be superior to methods like Fourier analysis that need longer

segments.



The choice of temporal filtering method, particularly for

research studies, should also consider the need to detect non-CNS

artifacts. A single band-pass filter cannot identify a broadband artifact like

EMG; a representative set of such filters is needed. Similarly, when

autoregressive parameters are used as signal features, additional

spectral-band analyses are needed to detect artifacts like EG.

For SCP recording, the focus on extremely low frequency

activity requires attention to eye-movement and other low-frequency

artifacts like those due to amplifier drift or changes in skin. The signal-to-

noise ratios of evoked time-domain signals like P300 can be enhanced by

averaging. The accompanying loss in communication rate may be

minimized by overlapping the trials. A variety of methods have been

proposed for detecting signals in single trials. These methods have yet to

be extensively applied in BCI research. Thus, their potential usefulness is

unclear. Invasive method using epidural, subdural, or intracortical

electrodes might give better signal-to-noise ratios than non invasive

methods using scalp electrodes. At the same time, the threshold for their

use will presumably be higher. They will be used only when they can

provide communication clearly superior to that provided by non invasive

methods, or when they are needed to avoid artifacts or problems that can

impede non invasive methods (e.g. uncontrollable head and neck EMG in

a user with cerebral palsy).

In short, the digitized signals are subjected to one or more of

a variety of feature extraction procedures, such as spatial filtering,

voltage amplitude measurements, spectral analysis, or single-neuron

separation. This analysis extracts the signal features that (hopefully)

encode the user’s message or commands. BCIs can use signal features

that are in the time domain (e.g. evoked potential amplitude or neuronal

firing rates) or the frequency domain (e.g. mu or beta – rhythm



amplitudes or neuronal firing rates) or the frequency domain and

frequency-domain signal features, and might thereby improve

performance. In general, the signal features used in present-day BCIs

reflect identifiable brain events like the firing of a specific control neuron

or the synchronized and rhythmic synaptic activation in sensorimotor

cortex that produces a mu rhythm.

Spatial filters derive signal features by combining data from

two or more locations so as to focus on activity with a particular spatial

distribution. The simplest spatial filter is the bipolar derivation, which

derives the first spatial derivative and thereby enhances differences in the

voltage gradient in one direction.

Spectral analysis is used to identify the frequency components

having a favorable response to the user intension. Oscillatory signals like

the mu rhythm can be measured by the integrated output of a band-pass

filter or by the amplitude in specific spectral bands of fourier or

autoregressive analysis. The signal to-noise ratios of evoked time-domain

signals like P300 can be enhanced by averaging.

3.1.3TRANSLATION ALGORITHM

BCI translation algorithms convert independent variable, that is,

signal features such as rhythm amplitude or neuronal firing rates, into

dependent variables (i.e. device control commands). Commands may be

continuous (e.g. vertical cursor movements) or discrete (e.g. letter

selection). They should be as independent of each other (i.e. orthogonal)

as possible, so that, for example, vertical cursor movement and horizontal

cursor movement do not depend on each other.

The success of translation algorithm is determined by the

appropriateness of its selection of signal features, by how well it



encourages and facilities the user’s control of these features, and by how

effectively it translates this control into device commands. If the user has

no control (i.e. if the user’s intent is not correlated with the signal

features) the algorithm can do nothing, and the BCI will not work. If the

user has some control, the algorithm can do a good or bad job of

translating that control into device control.

Initial selection o signal features for the translation algorithm

can be based on standard guideline (e.g. the known locations and

temporal and spatial frequencies of mu and beta rhythms) supplemented

by operator inspection of initial topographical and spectral data from each

user. These methods may be supplemented or even wholly replaced by

automated procedures. For example, Pregenzer used the learning vector

quantizer (LVQ) to select optimal electrode positions and frequencies

band for each user.

Extant BCIs use a variety of translation algorithms, ranging

from linear equations, to discriminant analysis, to neural networks. In the

simples case, in which only a single signal feature is used, the output of

the translation algorithm can be a simple linear function of the feature

value (e.g. a linear function of murhythm amplitude). The algorithm needs

to use appropriate values for the intercept and the slope of this function.



If the command is vertical cursor movement, the intercept

should ensure that upward and downward movement are equally possible

for the user found that the mean value of the signal feature over some

interval of immediately preceding performance provides a good estimate

o the proper intercept. The slope determines the scale of the command

(e.g. the sped of cursor movement).

When a single feature is used to select among more than two

choices, the slope also affects the relative accessibility of the choices. A

wide variety of more complex translation algorithms are possible. These

include supervised learning approaches such as linear discriminate

analysis and non-linear discriminate analysis.

The evaluation of a translation algorithm reduces to

determining how well it accomplishes the 3 levels of adaptation to the

individual user; continuing adaptation to spontaneous changes in the

user’s performance (e.g. fatigue’ level of attention); and continuing

adaptation that encourages and guides the user’s adaptation to the BCI

(i.e. user training).

Up to the present, most evaluation have concentrated on the

first and simplest level of adaptation. In these evaluations, alternative

algorithms are applied offline to a body of data gathered from one or

more users. Typically, portions of the data are used to the parameters of

the algorithm, which is hen applied to the rest of the data (i.e. the test

data). The algorithm is rated according to the accuracy with which it

derives the user’s intent from the test data. While such evaluations are

convenient and certainly valuable in making gross distinctions between

algorithms, they do not take into account spontaneous changes in he

signal features, nor can they assess user adaptation to the algorithm.



The second level of adaptation continual adjustments for

spontaneous changes in signal features-can be addressed by offline

analysis that mimics the online situation, that is, if adaptation is based on

earlier data and applied to later data. This analysis needs substantial

bodies of data gathered over substantial periods of time, so that all major

kinds of spontaneous variation can be assessed. The need for this second

level of adaptation tends to favor simpler algorithms. Parameter

adaptation is likely to be more difficult and more vulnerable to instabilities

for complex algorithms like those using neural networks or non-linear

equations, than it is for simple algorithms like those using linear equations

with relatively few variables.

The third level of adaptation – adaptation to the user’s

adaptation to the BCI system – is not accessible to offline evaluation.

Because this level responds to and affects the continua interactions

between the user and the BCI, it can only be assessed online. The goal of

this adaptation is to induce the user to develop and maintain the highest

possible level of correlation between his or her intent and the signal

features that the BCI employs to decipher that intent. The algorithm can

presumably accomplish these aims by rewarding better performance – by

moving the cursor or selecting the letter more quickly whne the signal

feature has a stronger correlation with intent. At the same time, such

efforts at shaping user performance risk making the task too difficult.

As with acquisition of conventional skills, frustration, or fatigue

can degrade performance. Particularly in the first stages of training, the

user is easily overwhelmed by the difficulty of the task. User success may

correlate with self-perception of brain states, and may be promoted by

procedure that increases this perception.

Because the translation algorithm’s adaptations are likely to

shape the user’s adaptations, and because users are likely to differ from



one another, the selection of methods for this third level of adaptation

inevitably requires prolonged online studies in large numbers of

representative users. This level of adaptation might also help address the

problem of artifacts, such as EMG or EOG for scalp EEG or extraneous

neuronal activity for neuronal recording. It may be possible to include the

user to reduce or eliminate such artifacts by making them impediments to

performance.

Thus, a specific measure of EMG activity, like amplitude in a

high frequency band at a suitable location, could be monitored, and, by

exceeding a criterion value, could halt BCI operation. The mutual

adaptation of user and BCI is likely to be important even for BCIs that use

signal features (e.g. P300 evoked potentials, or mu-or beta-rhythm

amplitude changes accompanying specific motor imagery) that are

already present in users at the very beginning of training. Once these

features are used for communication and control, they can be expected to

change. Like the activity responsible for the brain’s neuromuscular

outputs, the electrophysiological phenomena are likely to be continuously

adjusted on the basis of feedback.



The process of mutual adaptation of the user to the system and

the system to the user is likely to be a fundamental feature of the

operation of any BCI system. Thus, the value of starting from signal

features that are already correlated with specific intents in the native user

(e.g. P300) is an empirical issue. That is, does BCI training that begins

with such feature ultimately lead to faster and more accurate

communication and control than does training that begins with other

features? These adaptations by the translation algorithm may be more

difficult in actual BCI applications than in the laboratory. In the usual

laboratory situation, user intent is defined by the research laboratory. In

real life, the user decides what to select, so that the translation algorithm

does not have this knowledge and adaptation is therefore more difficult.

Possible solutions are to configure applications so as to insure fairly

predictable sets of past intents, to incorporate calibration routines that

consist of series of trials with defined intents, and/or to include methods

for error correction (e.g. a backspace key) that permit the translation

algorithm to assume that all or most final selections are correct.

Unsupervised learning approaches, like cluster or principal components

analysis, which can be trained without knowledge of correct results, might

also be effective.

2.1.4. FEEDBACK

For most current BCIs the output device is a computer screen

and the output is the selection of targets, letters, or icons presented on it.

Selection is indicated in various ways (e.g. the letter flashes). Some BCIs

also provide additional, interim output, such as cursor movement toward

the item prior to its selection. In addition to being the intended product of

BCI operation, this output is the feedback that the brain uses to maintain

and improve the accuracy and speed of communication. Initial studies are

also exploring BCI control of a neuroprosthesis that provides hand closure



to people with cervical spinal cord injuries in this prospective BCI

application, the output device is the user’s own hand.



3. APPLICATIONS OF BRAIN-COMPUTER

INTERFACE

Brain-Computer Interface (BCI) is a system that acquires and

analyzes neural signals with the goal of creating a communication channel

directly between the brain and the computer. Such a channel potentially

has multiple uses. The current and the most important application of a BCI

is the restoration of communication channel for patients with locked-in-

syndrome.

1) Patients with conditions causing severe communication disorders:

– Advanced Amyotrophic Lateral Sclerosis (ALS)

– Autism

– Cerebral Palsy

– Head Trauma

– Spinal Injury

The output signals are evaluated for different purpose such as

cursor control, selection of letters or words.

2) Military Uses:

The Air Force is interested in using brain-body actuated control to

make faster responses possible for fighter pilots. While brain-body

actuated control is not a true BCI, it may still provide motivations for why

a BCI could prove useful in the future.A combination of EEG signals and

artifacts (eye movement, body movement, etc.) combine to create a

signal that can be used to fly a virtual plane.

3) Bioengineering Applications:



Assist devices for the disabled. Control of prosthetic aids.

4) Control of Brain-operated wheelchair.

5) Multimedia & Virtual Reality Applications:

Virtual Keyboards

Manipulating devices such as television set, radio, etc.

Ability to control video games and to have video games react

to actual EEG signals.

4. PRINCIPLES OF

ELECTROENCEPHALOGRAPHY

4.1 The Nature of the EEG signals.

The electrical nature of the human nervous system has been

recognized for more than a century. It is well known that the variation of

the surface potential distribution on the scalp reflects functional activities

emerging from the underlying brain. This surface potential variation can

be recorded by affixing an array of electrodes to the scalp, and measuring

the voltage between pairs of these electrodes, which are then filtered,

amplified, and recorded. The resulting data is called the EEG.

Configurations of electrodes usually follow the International 10-20 system

of placement. The 10-20 System of Electrode Placement, which is based

on the relationship between the location of an electrode and the

underlying area of cerebral cortex (the "10" and "20" refer to the 10% or

20% interelectrode distance).



Figure 4: showing electrodes in scalp

Figure 5: A detailed view for electrodes in scalp



The extended 10-20 system for electrode placement. Even numbers

indicate electrodes located on the right side of the head while odd

numbers indicate electrodes on the left side. The letter before the

number indicates the general area of the cortex the electrode is located

above. A stands for auricular,C for central, Fp for prefrontal, F for frontal, P

for parietal, O for Occipital, and T for temporal. In addition, electrodes for

recording vertical and horizontal electrooculographic (EOG) movements

are also place. Vertical EOG electrodes are placed above and below an

eye and horizontal EOG electrodes are placed on the side of both eyes

away from the nose.

Nowadays, modern techniques for EEG acquisition collect these

underlying electrical patterns from the scalp, and digitalize them for

computer storage. Electrodes conduct voltage potentials as microvolt

level signals, and carry them into amplifiers that magnify the signals

approximately ten thousand times. The use of this technology depends

strongly on the electrodes positioning and the electrodes contact. For this

reason,electrodes are usually constructed from conductive materials, such

us gold or silver chloride, with an approximative diameter of 1 cm, and

subjects must also use a conductive gel on the scalp to maintain an

acceptable signal to noise ratio.

4.2 EEG wave groups.

The analysis of continuous EEG signals or brain waves is complex, due to

the large amount of information received from every electrode. As a

science in itself, it has to be completed with its own set of perplexing

nomenclature. Different waves, like so many Radio stations, are

categorized by the frequency of their emanations and, in some cases, by

the shape of their waveforms. Although none of these waves is ever

emitted alone, the state of consciousness of the individuals may make



one frequency range more pronounced than others. Five types are

particularly important:

• BETA. The rate of change lies between 13 and 30 Hz, and usually

has a low voltage between 5-30 V BETA. The rate of change lies

between 13 and 30 Hz, and usually has a low voltage between 5-30

V Beta is the brain wave usually associated with active thinking,

active attention, focus on the outside world or solving concrete

problems. It can reach frequencies near 50 hertz during intense

mental activity.

• ALPHA. The rate of change lies between 8 and 13 Hz, with 30-50 V

amplitude. Alpha waves have been thought to indicate both a

relaxed awareness and also in attention. They are strongest over

the occipital (back of the head) cortex and also over frontal cortex.

Alpha is the most prominent wave in the whole realm of brain

activity and possibly covers a greater range than has been

previously thought of. It is frequent to see a peak in the beta range

as high as 20 Hz, which has the characteristics of an alpha state

rather than a beta, and the setting in which such a response

appears also leads to the same conclusion. Alpha alone seems to

indicate an empty mind rather than a relaxed one, a mindless state

rather than a passive one, and can be reduced or eliminated by

opening the eyes, by hearing unfamiliar sounds, or by anxiety or

mental concentration.

• THETA. Theta waves lie within the range of 4 to 7 Hz, with an

amplitude usually greater than 20 V. Theta arises from emotional

stress, especially frustration or disappointment.Theta has been also

associated with access to unconscious material, creative inspiration



and deep meditation. The large dominant peak of the theta waves is

around 7 Hz.

• DELTA. Delta waves lie within the range of 0.5 to 4 Hz, with variable

amplitude. Delta waves are primarily associated with deep sleep,

and in the waking state, were thought to indicate physical defects in

the brain. It is very easy to confuse artifact signals caused by the

large muscles of the neck and jaw with the genuine delta responses.

This is because the muscles are near the surface of the skin and

produce large signals whereas the signal which is of interest

originates deep in the brain and is severely attenuated in passing

through the skull. Nevertheless, with an instant analysis EEG, it is

easy to see when the response is caused by excessive movement.

• GAMMA. Gamma waves lie within the range of 35Hz and up. It is

thought that this band reflects the mechanism of consciousness -

the binding together of distinct modular brain functions into

coherent percepts capable of behaving in a re-entrant fashion

(feeding back on themselves over time to create a sense of stream-

of-consciousness).

• MU. It is an 8-12 Hz spontaneous EEG wave associated with motor

activities and maximally recorded over motor cortex. They diminish

with movement or the intention to move. Mu wave is in the same

frequency band as in the alpha wave, but this last one is recorded

over occipital cortex.

Most attempts to control a computer with continuous EEG

measurements work by monitoring alpha or mu waves, because people

can learn to change the amplitude of these two waves by making the



appropriate mental effort. A person might accomplish this result, for

instance, by recalling some strongly stimulating image or by raising his or

her level of attention.



5. NEUROPSYCHOLOGICAL SIGNALS USED IN

BCI APPLICATIONS

5.1 Generation of Neuropsychological Signals

Interfaces based on brain signals require on-line detection of mental

states from spontaneous activity: different cortical areas are activated

while thinking different things (i.e. a mathematical computation, an

imagined arm movement, a music composition, etc). The information of

these "mental states" can be recorded with different methods.

Neuropsychological signals can be generated by one or more of the

following three:

• implanted methods

• evoked potentials (also known as event related potentials)

• operant conditioning

Both evoked potential and operant conditioning methods are

normally externally-based BCIs as the electrodes are located on the scalp.

The table describes the different signals in common use. It may be noted

that some of the described signals fit into multiple categories.

Implanted methods use signals from single or small groups of neurons in

order to control a BCI. In most cases, the most suitable option for placing

the electrodes is the motor cortex region, because of its direct relevance

to motor tasks, its relative accessibility compared to motor areas deeper

in the brain, and the relative ease of recording from its large pyramidal

cells. These methods have the benefit of a much higher signal-to-noise

ratio at the cost of being invasive. They require no remaining motor

control and may provide either discrete or continuous control.

Evoked potentials (EPs) are brain potentials that are evoked by the

occurrence of a sensory stimulus. They are usually obtained by averaging

a number of brief EEG segments time-registered to a stimulus in a simple



task. In a BCI, EPs may provide control when the BCI application produces

the appropriate stimuli. This paradigm has the benefit of requiring little to

no training to use the BCI at the cost of having to make users wait for the

relevant stimulus presentation. EPs offer discrete control for almost all

users.



Exogenous components, or those components influenced primarily

by physical stimulus properties, generally take place within the first 200

milliseconds after stimulus onset. These components include a Negative

waveform around 100 ms (N1) and a Positive waveform around 200 ms

after stimulus onset (P2). Visual evoked potentials (VEPs) fall into this

category. Uses short visual stimuli in order to determine what command

an individual is looking at and therefore wants to pick. Using VEPs has the

benefit of a quicker response than longer latency components. The VEP

requires subject to have good visual control in order to look at the

appropriate stimulus and allows for discrete control.

One commonly studied ERP in BCI is a component called the P300.It

is a positive peak in the potential that reaches a maximum of about 300

ms after the stimulus is presented. The P3 has been shown to be fairly

stable in locked-in patients, reappearing even after severe brain injuries.



Figure 6 : (Solid line) The general form of the P3 component of the evoked

potential (EP). The P3 is a cognitive EP that appears approximately 300

ms after

a task relevant stimulus. (Dotted line) The general form of a non-task

related response.

Operant conditioning is a method for modifying the behavior (an

operant), which utilizes contingencies between a discriminative stimulus,

an operant response, and a reinforcer to change the probability of a

response occurring again in a given situation. In the BCI framework, it is

used to train the patients to control their EEG. As it is presented in,

several methods use operant conditioning on spontaneous EEG signals for

BCI control. The main feature of this kind of signals is that it enables

continuous rather than discrete control. This feature may also serve as a

drawback: continuous control is fatiguing for subjects and fatigue may

cause changes in performance since control is learned.



5.2 Common Neuropsychological Signals Used I n



BCIs

6. EEG SIGNAL PRE-PROCESSING

One of the main problems in the automated EEG analysis is the

detection of the different kinds of interference waveforms (artifacts)

added to the EEG signal during the recording sessions. These interference

waveforms, the artifacts, are any recorded electrical potentials not

originated in brain. There are four main sources of artifacts emission:

1. EEG equipment.

2. Electrical interference external to the subject and recording

system.

3. The leads and the electrodes.

4. The subject her/himself: normal electrical activity from the heart,

eye blinking, eyes movement, and muscles in general.

In case of visual inspections, the artifacts can be quite easily detected

by EEG experts. However, during the automated analysis these signal

patterns often cause serious misclassifications thus reducing the clinical

usability of the automated analyzing systems. Recognition and elimination

of the artifacts in real – time EEG recordings is a complex task, but

essential to the development of practical systems.

6.1 Classical Methods for removing eyeblink artifacts :

• Rejection methods consist of discarding contaminated EEG, based on

either automatic or visual detection. Their success crucially depends

on the quality of the detection, and its use depends also on the

specific application for which it is used.Thus, although for epileptic

applications, it can lead to an unacceptable loss of data, for others,

like a Brain Computer interface, its use can be adequate.

• Subtraction methods are based on the assumption that the

measured EEG is a linear combination of an original EEG and a



signal caused by eye movement, called EOG (electrooculogram).

The EOG is a potential produced by movement of the eye or eyelid.

The original EEG is hence recovered by subtracting separately

recorded EOG from the measured EEG, using appropriate weights

(rejecting the influence of the EOG on particular EEG channels).



6 .2 EEG Feature Extraction

For the analysis of oscillatory EEG components, the following

preprocessing methods:

1) calculation of band power in predefined, subject-specific frequency

bands in intervals of 250 (500) ms.

2) adaptive autoregressive (AAR) parameters estimated for each

iteration with the recursive least squares algorithm (RLS).

3) calculation of common spatial filters (CSP).

Band power at each electrode position is estimated by first digitally

bandpass filtering the data, squaring each sample and then averaging

over several consecutive samples. Before the band power method is used

for classification, first the reactive frequency bands must be selected for

each subject.Based on these training data, the most relevant frequency

components can be determined by using the distinction sensitive learning

vector quantization (DSLVQ) algorithm. This method uses a weighted

distance function and adjusts the influence of different input features

(e.g., frequency components) through supervised learning. When DSLVQ

is applied to spectral components of the EEG signals (e.g., in the range

from 5 to 30 Hz), weight values of individual frequency components

according to their relevance for the classification task are obtained.

The AAR parameters, in contrast, are estimated from the EEG

signals limited only by the cutoff frequencies, providing a description of

the whole EEG signal. Thus, an important advantage of the AAR method is

that no a priori information about the frequency bands is necessary .

For both approaches, two closely spaced bipolar recordings from the

left and right sensorimotor cortex were used. In further studies, spatial

information from a dense array of electrodes located over central areas

was considered to improve the classification accuracy. For this purpose,

the CSP method was used to estimate spatial filters that reflect the

specific activation of cortical areas during hand movement imagination.



Each electrode is weighted according to their importance for the

classification. The method makes a decomposition of EEG data into spatial

patterns which are extracted from two populations (EEG data during left

and right movement imagination) and is based on simultaneous

diagonalization of two covarinance matrices. The pattern maximizes the

difference between left and right population and the only information

contained in these patterns is where the variance of the EEG varies most

when comparing two conditions.

7. SIGNAL CLASSIFICATION PROCEDURES

An important step toward real-time processing and feedback

presentation is the setup of a subject-specific classifier. For this, two

different approaches are followed:

i) neural network based classification, e.g. a learning vector quantization

(LVQ)

ii) linear discriminant analysis (LDA)

Learning Vector Quantization (LVQ) has proven to be an effective

classification procedure. LVQ is shown to be comparable with other neural

network algorithms for the task of classifying EEG signals, yielding

approximately 80% classification accuracy for three out of the four

subjects tested when differentiating between two different mental tasks.

LVQ was mainly applied to online experiments with delayed feedback

presentation. In these experiments, the input features were extracted

from a 1-s epoch of EEG recorded during motor imagery. The EEG was

filtered in one or two subject-specific frequency bands before calculating

four band power estimates, each representing a time interval of 250 ms,

per EEG channel and frequency range. Based on these features, the LVQ

classifier derived a classification and a measure describing the certainty

of this classification, which in turn was provided to the subject as a

feedback symbol at the end of each trial.



In experiments with continuous feedback based on either AAR

parameter estimation or CSP’s, a linear discriminant classifier has usually

been applied for on-line classification. The AAR parameters of two EEG

channels or the variance time series of the CSP’s are linearly combined

and a time-varying signed distance (TSD) function is calculated. With this

method it is possible to indicate the result and the certainty of

classification, e.g., by a continuously moving feedback bar. The different

methods of EEG preprocessing and classification have been compared in

extended on-line experiments and data analyzes. These experiments were

carried out using a newly developed BCI system running in real-time

under Windows with a 2, 8, or 64 channel EEG amplifier . The installation

of this system, based on a rapid prototyping environment, includes a

software package that supports the real-time implementation and testing

of different EEG parameter estimation and classification algorithms.



8 . EXISTING BCI SYSTEMS

8.1 The Brain Response Interface

Sutter's Brain Response Interface (BRI) is a system that takes

advantage of the fact that large chunks of the visual system are devoted

to processing information from the foveal region. The BRI uses visually

evoked potentials (VEP's) produced in response to brief visual stimuli.

These EP's are then used to give a discrete command to pick a certain

part of a computer screen. This system is one of the few that have been

tested on severely handicapped individuals. Word processing output

approaches 10-12 words/min. and accuracy approaches 90% with the use

of epidural electrodes. This is the only system mentioned that uses

implanted electrodes to obtain a larger, less contaminated signal. A BRI

user watches a computer screen with a grid of 64 symbols (some of which

lead to other pages of symbols) and concentrates on the chosen symbol.

A specific subgroup of these symbols undergoes a equiluminant red/green

fine check or plain color pattern alteration in a simultaneous stimulator

scheme at the monitor vertical refresh rate (40-70 frames/s). Sutter

considered the usability of the system over time and since color alteration

between red and green was almost as effective as having the monitor

flicker, he chose to use the color alteration because it was shown to be

much less fatiguing for users. The EEG response to this stimulus is

digitized and stored. Each symbol is included in several different

subgroups and the subgroups are presented several times. The average

EEG response for each subgroup is computed and compared to a

previously saved VEP template (obtained in an initial training session),

yielding a high accuracy system.This system is basically the EEG version

of an eye movement recognition system and contains similar problems

because it assumes that the subject is always looking at a command on

the computer screen. On the positive side, this system has one of the best



recognition rates of current systems and may be used by individuals with

sufficient eye control. Performance is much faster than most BCIs, but is

very slow when compared to the speed of a good typist (80 words/min.).

The system architecture is advanced. The BRI is implemented on a

separate processor with a Motorola 68000 CPU. A schematic of the system

is shown in Figure. The BRI processor interacts with a special display

showing the BRI grid of symbols as well as a speech synthesizer and

special keyboard interface. The special keyboard interface enables the

subject to control any regular PC programs that may be controlled from

the keyboard. In addition, a remote control is interfaced with the BRI in

order to enable the subject to control a TV or VCR. Since the BRI processor

loads up all necessary software from the hard drive of a connected PC, the

user may create or change command sequences. The main drawback of

the system architecture is that it is based on a special hardware interface.

This may be problematic when changes need to be made to the system

over time.



Figure 7: A schematic of the Brain Response Interface (BRI) system



8 .2 P3 Character Recognition

In a related approach, Farwell and Donchin use the P3 evoked

potential. A 6x6 grid containing letters from the alphabet is displayed on

the computer monitor and users are asked to select the letters in a word

by counting the number of times that a row or column containing the

letter flashes. Flashes occur at about 10 Hz and the desired letter flashes

twice in every set of twelve flashes. The average response to each row

and column is computed and the P3 amplitude is measured. Response

amplitude is reliably larger for the row and column containing the desired

letter. After two training sessions, users are able to communicate at a rate

of 2.3 characters /min, with accuracy rates of 95%. This system is

currently only used in a research setting. A positive aspect of using a

longer latency component such as the P3 is that it enables differentiating

between when the user is looking at the computer screen or looking

someplace else (as the P3 only occurs in certain stimulus conditions).

Unfortunately, this system is also agonizingly slow, because of the need to

wait for the appropriate stimulus presentation and because the stimuli are

averaged over trials. While the experimental setup accomplishes its main

goal of showing that the P3 may be used for a BCI interface, the

subjective experiences of a subject with this system have yet to be

considered. The 10 Hz rate of flashing may fatigue users as Sutter

mentions and this rate of flashing may cause epilepsy in some subjects.

8.3 ERS/ERD Cursor Control

Pfurtscheller and his colleagues take a different approach.Using

multiple electrodes placed over sensorimotor cortex they monitor event-

related synchronization/desynchronization (ERS/ERD). In all sessions,

epochs with eye and muscle artifact are automatically rejected. This

rejection can slow subject performance speeds.As this is a research

system, the user application is a simple screen that allows control of a

cursor in either the left or right direction. In one experiment, for a single



trial the screen first appears blank, then a target box is shown on one side

of the screen. A cross hair appears to let the user know that he/she must

begin trying to move the cursor towards the box. Feedback may be

delayed or immediate and different experiments have slightly different

displays and protocols. After two training sessions, three out of five

student subjects were able to move a cursor right or left with accuracy

rates from 89-100%. Unfortunately, the other two students performed at

60% and 51%. When a third category was added for classification,

performance dropped to a low of 60% in the best case. The architecture of

this BCI now contains a remote control interface that allows controlling the

system over a phone line, LAN, or Internet connection.

This allows maintenance to be done from remote locations. The

system may be run from a regular PC, a notebook, or an embedded

computer and is being tested for opening and closing a hand orthesis in a

patient with a C5 lesion. From this information, it appears that the user

application must be independent from the BCI, although it is possible that

two different BCI programs were constructed.

This BCI system was designed with the following requirements in mind:

1. The system must be able to record, analyze, and classify EEG-data in

real- time.

2. The classification results must have the ability to be used to control a

device on-line.

3. The system must have the ability to have different experimental

paradigms Sutter's Brain Response Interface (BRI) is a system that takes

advantage of the fact that large chunks of the visual system are devoted

to processing information from the foveal region. The BRI uses visually

evoked potentials (VEP's) produced in response to brief visual stimuli.

These EP's are then used to give a discrete command to pick a certain

part of a computer screen. This system is one of the few that have been

tested on severely handicapped individuals. Word processing output



approaches 10-12 words/min. and accuracy approaches 90% with the use

of epidural electrodes. This is the only system mentioned that uses

implanted electrodes to obtain a larger, less contaminated signal. A BRI

user watches a computer screen with a grid of 64 symbols (some of which

lead to other pages of symbols) and concentrates on the chosen symbol.

A specific subgroup of these symbols undergoes a equiluminant red/green

fine check or plain color pattern alteration in a simultaneous stimulator

scheme at the monitor vertical refresh rate (40-70 frames/s). Sutter

considered the usability of the system over time and since color alteration

between red and green was almost as effective as having the monitor

flicker, he chose to use the color alteration because it was shown to be

much less fatiguing for users. The EEG response to this stimulus is

digitized and stored. Each symbol is included in several different

subgroups and the subgroups are presented several times. The average

EEG response for each subgroup is computed and compared to a

previously saved VEP template (obtained in an initial training session),

yielding a high accuracy system.This system is basically the EEG version

of an eye movement recognition system and contains similar problems

because it assumes that the subject is always looking at a command on

the computer screen. On the positive side, this system has one of the best

recognition rates of current systems and may be used by individuals with

sufficient eye control. Performance is much faster than most BCIs, but is

very slow when compared to the speed of a good typist (80 words/min.).

The system architecture is advanced. The BRI is implemented on a

separate processor with a Motorola 68000 CPU. A schematic of the system

is shown in Figure. The BRI processor interacts with a special display

showing the BRI grid of symbols as well as a speech synthesizer and

special keyboard interface. The special keyboard interface enables the

subject to control any regular PC programs that may be controlled from

the keyboard. In addition, a remote control is interfaced with the BRI in



order to enable the subject to control a TV or VCR. Since the BRI processor

loads up all necessary software from the hard drive of a connected PC, the

user may create or change command sequences. The main drawback of

the system architecture is that it is based on a special hardware interface.

This may be problematic when changes need to be made to the system

over time.

8.4 A Steady State Visual Evoked Potential BCI

Middendorf and colleagues use operant conditioning methods in

order to train volunteers to control the amplitude of the steady-state

visual evoked potential (SSVEP) to florescent tubes flashing at 13.25 Hz.

This method of control may be considered as continuous as the amplitude

may change in a continuous fashion. Either a horizontal light bar or audio

feedback is provided when electrodes located over the occipital cortex

measure changes in signal amplitude. If the VEP amplitude is below or

above a specified threshold for a specific time period, discrete control

outputs are generated. After around 6 hours of training, users may have

an accuracy rate of greater than 80% in commanding a flight simulator to

roll left of right. In the flight simulator, the stimulus lamps are located

adjacent to the display behind a translucent diffusion panel. As operators

increase their SSVER amplitude above one threshold, the simulator rolls to

the right. Rolling to the left is caused by a decrease in the amplitude. A

functional electrical stimulator (FES), has been integrated for use with this

BCI. Holding the SSVER above a specified threshold for one second,

causes the FES to turn on. The activated FES then starts to activate at the

muscle contraction level and begins to increase the current, gradually

recruiting additional muscle fibers to cause knee extension. Decreasing

the SSVER for over a second, causes the system to deactivate, thus

lowering the limb. Recognizing that the SSVEP may also be used as a

natural response, Middendorf and his colleagues have recently

concentrated on experiments involving the natural SSVEP. When the



SSVEP is used as a natural response, virtually no training is needed in

order to use the system. The experimental task for testing this method of

control has been to have subjects select virtual buttons on a computer

screen.

The luminance of the virtual buttons is modulated, each at a

different frequency to produce the SSVEP. The subject selects the button

by simply looking at it as in Sutter’s Brain Response Interface. From the 8

subjects participating in the experiment, the average percent correct was

92% with an average selection time of 2.1 seconds. Middendorf’s group

has advocated using visual evoked potentials, in this manner as opposed

to their previous work on training control of the SSVEP, for multiple

reasons. Using an inherent response means that less time is spent on

training. The main drawback of this group’s approach appears to be that

they flicker light at different frequencies. Sutter solved the problem of

flicker-related fatigue by using alternating red/green illumination. The

main frequency of stimulus presentation at 13.25 Hz may also cause

epilepsy.

8.5 Mu Rhythm Cursor Control

Wolpaw and his colleagues free their subjects from being tied to a

flashing florescent tube by training subjects to modify their mu rhythm.

This method of control is continuous as the mu rhythm may be altered in

a continuous manner. It can be attenuated by movement and tactile

stimulation as well as by imagined movement. A subject's main task is to

move a cursor up or down on a computer screen. While not all subjects

are able to learn this type of biofeedback control, the subjects that do

perform with accuracy greater than or equal to 90%. These experiments

have also been extended to two-dimensional cursor movement, but the

accuracy of this is reported as having “not reached this level of accuracy”

when compared to the one-dimensional control .Since the mu rhythm isn't

tied to an external stimulus, it frees the user from dependence on



external events for control. The BCI system consists of a 64-channel EEG

amplifier, two 32-channel A/D converter boards, a TMS320C30-based DSP

board, and a PC with two monitors. One monitor is used by the subject

and one by the operator of the system . Only a subset of the 64-channels

are used for control, but the number of channels allows recognition to be

adjusted to the unique topographical features of each subject’s head. The

DSP board is programmable in the C-language, enabling testing of all

program code prior to running it on the DSP board. Software is also

programmed in C in order to create consistency across system modules.

The architecture of the system is shown in Figure. Four processes run

between the PC and the DSP board. As signal acquisition occurs, an

interrupt request is sent from the A/D board to the DSP at the end of A/D

conversion. The DSP then acquires the data from all requested channels

sequentially and combines them to derive the one or more EEG channels

that control cursor movement. This is the data collection process.

A second process then takes care of performing a spectral analysis

on the data. When this analysis is completed, the results are moved to

dual-ported memory and an interrupt to the PC is generated. A

background process on the PC then acquires spectral data from the DSP

board and computes cursor movement information as well as records

relevant trial information.



Figure 8 : A schematic of the mu rhythm cursor control system architecture.

The system contains four parallel processes.

This process runs at a fixed interval of 125 msec. The fourth process

handles thegraphical user interfaces for both the operator and the subject

and records data to disk.The separation of data collection and analysis

enables different algorithms to be inserted for processing the EEG signals.

All algorithms are written in C, which is much easier to program in than

Assembly language, but is not as easy as the commercial Matlab ®

scripting language and environment, which contains many helpful

functions for mathematically processing data. The third and fourth

processes contain design decisions that may make maintenance and

flexibility difficult. The graphical user interface is tied to data storage.



Conversion of EEG signals to cursor control numbers happens over

the DSP foreground/background processes and in the PC background

process. This lack of encapsulation promises to make changing the

application and signal processing difficult if such changes are planned.

8.6 The Thought Translation Device

As another application used with severely handicapped individuals,

the Thought Translation Device has the distinction of being the first BCI to

enable an individual without any form of motor control to communicate

with the outside world. Out of six patients with ALS, 3 were able to use the

Thought Translation Device. Of the other three, one lost motivation and

later died and another discontinued use of the Thought Translation Device

part way through training, and then later was unable to regain control.

The paper implies that users do not want to use the BCI unless they

absolutely must, but does not disambiguate subjective user satisfaction of

the system from general user depression. The training program may use

either auditory or visual feedback. The slow cortical potential is extracted

from the regular EEG on-line, filtered, corrected for eye movement

artifacts, and fed back to the patient. In the case of auditory feedback, the

positivity/negativity of a slow cortical potential is represented by pitch.

When using visual feedback, the target positivity/negativity is represented

by a high and low box on the screen. A ball-shaped light moves toward or

away from the target box depending on a subject’s performance. The

subject is reinforced for good performance with the appearance of a

happy face or a melodic sound sequence. When a subject performs at

least 75% correct, he/she is switched to the language support program.

At level one, the alphabet is split into two halves (letter-banks) which are

presented successively at the bottom of the screen for several seconds. If

the subject selects the letter-bank being shown by generating a slow

cortical potential shift, that side of the alphabet is split into two halves

and so on, until a single letter is chosen. A “return function” allows the



patient to erase the last written letter. These patients may now write

email in order to communicate with other ALS patients world-wide. An

Internet version of the thought translation device is under construction.

The authors comment that patients refuse to use pre-selected word

sequences because they feel less free in presenting their own intentions

and thoughts.



8.7 An Implanted BCI

The implanted brain-computer interface system devised by Kennedy

and colleagues has been implanted into two patients. These patients are

trained to control a cursor with their implant and the velocity of the cursor

is determined by the rate of neural firing. The neural waveshapes are

converted to pulses and three pulses are an input to the computer mouse.

The first and second pulses control X and Y position of the cursor and a

third pulse as a mouse click or enter signal.The patients are trained using

software that contains a row of icons representing common phrases (Talk

Assist developed at Georgia Tech), or a standard ‘qwerty’ or alphabetical

keyboard (Wivik software from Prentke Romich Co.). When using a

keyboard, the selected letter appears on a Microsoft Wordpad screen.

When the phrase or sentence is complete, it is output as speech using

Wivox software from Prentke Romich Co. or printed text. There are two

paradigms using the Talk Assist program and a third one using the visual

keyboard. In the first paradigm, the cursor moves across the screen using

one group of neural signals and down the screen using another group of

larger amplitude signals. Starting in the top left corner, the patient enters

the leftmost icon. He remains over the icon for two seconds so that the

speech synthesizer is activated and phrases are produced. In the second

paradigm, the patient is expected to move the cursor across the screen

from one icon to the other. The patient is encouraged to be as accurate as

possible, and then to speed up the cursor movement while attempting to

remain accurate. In the third paradigm, a visual keyboard is shown and

the patient is encouraged to spell his name as accurately and quickly as

possible and then to spell anything

else he wishes.This system uses commercially available software and thus

the BCI implementation does not have to worry about maintenance of the

user application. Unfortunately, the maximum communication rate with

this BCI has been around 3 characters per minute. This is the same rate as



quoted for EMG-based control with patient JR and is comparable with the

rates achieved by externally-based BCI systems. Kennedy has founded

Neural Signals, Inc. in order to help create hardware and software for

locked-in individuals and the company is continually looking for methods

to improve control. JR now has access to email and may be contacted

through the email address shown on the company’s web site.



9. Non-Invasive Vs Invasive Signal Detection

Non-Invasive

Pros

No surgical risks

Cons

Low signal resolution

Greater interference from other signals

Interfaces must be routinely cleaned and changed

Invasive

Pros

Higher resolution recording

Less interference from other signals

Faster communication possible

Cons

Determining which neurons to record from

Surgical risks



10 . R & D ACTIVITIES

Common standards and protocols: AS there is no coordinated

effort towards developing BCIs, each researcher builds the system using

custom design and protocols. This makes it difficult for universal use.

Therefore development of a set of common design standards and

communication protocols is one of the areas inviting attention of man.

1.Hybrid BCIs:

Most of the present day BCIs work based on only a single type of

brain wave like P300 evoked potential, Mu rhythms, Beta rhythms etc,

since it simplifies the feature extraction and translation processes. Even

though, attempts are being made towards developing hybrid brain

computer interfaces that detect multiple types of brain signals and decide

the user intention by combining features of all of them.

2.Silicon Implants :

As the digital circuit integration technology reaches higher levels of

integration densities, we can expect to have single chip computers that

can be implanted in the brain itself. It will lead to the era of cybernetic

organisms. Both the brain and artificial processor can work together to

achieve things that are impossible today.

3.More research into mental activities:

Thorough knowledge of the human psychology and neurological

features of brain is very much necessary for successful implementation of

brain computer interfaces. Hence research in this direction is also a part

of BCI research.

4.Improvements in signal detection, feature extraction:



Present day BSIs suffer a lot from lack of good signal detection

devices. Mostly we use EEgs for non invasive technology. The level of

detail that can be obtained by using EEGs is limited. Another option is to

use invasive technology by which electrodes are places inside brain. But it

requires surgery and therefore not suitable for common use. Also only a

few electrodes can be placed in this way. Hence newer methods for

detecting brain activities need to be developed. Changes are also being

made in the features extraction and translation algorithm parts for

ensuring better operation.

5.Cosmetic and economic improvements:

Cosmetic improvements are an absolute necessity for ensuring

universal acceptance and wider use. BCIs today are not considered to be

that fashionable, with those strange looking electrode caps and a large

number of wires running down the cap to the computer. Attempts are

being made to develop wearable BCIs. Economic efficiency is also a major

factor. Even the cheapest BCI system available costs about Rs. 30000

which is more than the price of a latest personal computer. In order to find

commercial applications, the cost of BCIs needs to be brought down.



11. CONCLUSION

BCI is a system that records electrical activity from the brain and

classifies these signals into different states. Few applications currently

being used have been discussed. Since the BCI enables people to

communicate and control appliances with just the use of brain signals it

opens many gates for disabled people. The possible future applications

are numerous. Even though this field of science has grown vastly in last

few years we are still a few steps away from the scene where people drive

brain-operated wheelchairs on the streets. New technologies need to be

developed and people in the neuroscience field need also to take into

account other brain imaging techniques, such as MEG and fMRI, to

develop the future BCI. As time passes BCI might be a part of our every

day lives. Who knows, in twenty years I’ll not have to type this report with

my fingers, but just the conscious control of my thoughts would be

enough.



12.REFERENCES

1. http://www.bci-info.org

2. http://www.ebme.com

3. http://www.google.com

4. http://www.bbci.org

5. http://www.wikipedia.com

6. http://www.youtube.com

7. Proprioceptive Feedback in BCI. Proceedings of the 4th international

IEEE EMBS conference on Neural Engineering, Antalya , Turkey, April29-

May2 2009.

8. A General Framework for Brain-Computer Interface Design. IEEE

Transactions on neural system and rehabilitation engineering, vol 11 no 1

march 2003 page 70-85.

9. A direct brain interface based on event related potentials. IEEE

Transaction 2000 Issue 8 Pages: 180-185.

10. Current trends in Brain-Computer Interface (BCI) research. IEEE

Transaction 2000 Issue 8 Pages: 216-219.


http://www.bbci.org/

http://www.google.com/

http://www.ebme.com/

http://www.bci-info.org/

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