High Transfer Rate, Real-time Brain-Computer Interface
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Transcript of High Transfer Rate, Real-time Brain-Computer Interface
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High Transfer Rate, Real-time Brain-Computer Interface
Machine-based learning techniques towards a practical spelling device for
the completely paralyzed
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April, 2005 ThinQ Innovation 2
Agenda
Brain Computer Interfaces – brief intro. Our system
– Overview, technical details– Machine learning – Support Vector Machines– Additional Bandwidth – Word Prediction – Results
Future Improvements, Q&A Demonstration at Psychology Lab
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April, 2005 ThinQ Innovation 3
BCIs – the Need
‘Locked-in’ patients
Example: J.D. Bauby, “The Diving Bell and the Butterfly”
Persistence of life –“butterfly” Extreme physical disability –
“diving bell”
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April, 2005 ThinQ Innovation 4
BCIs – the Need Amyotrophic Lateral Sclerosis
(ALS), aka Lou Gherig’s
– Degeneration of motor neurons, paralysis of voluntary muscles
– 120,000 diagnosed each year worldwide
– 2000 Canadians live with ALS right now
– Can leave patients ‘locked-in’– Cognitive and sensory functions
remain intact
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April, 2005 ThinQ Innovation 5
BCI(1): Slow Cortical Potentials (SCPs)
• Extensive training ~ 3 months using biofeedback mechanism
• Tested on ALS patients, learned to control SCPs
Ref: N. Birbaumer et al., “The thought translation device (TTD) for completely paralyzed patients,” IEEE Trans. Rehab. Eng., Vol. 8, pp. 190-193,June 2000.
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April, 2005 ThinQ Innovation 6
BCI(1): SCPs cont.• Most successful subject – artificially fed and respirated for 4 years
• After 3 months of training, wrote letter below
-Took 16 hours to write ~ 2 letters/minute
-Expresses thanks, wants to have a party
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April, 2005 ThinQ Innovation 7
BCI(2): Implants - Cyberkinetics Inc.
• BrainGate Neural Interface System: Mkt. cap ~$45mil.
• Control of cursor on PC using implant in motor cortex
• Undergoing limited clinical trials
• Limb movement possibilities
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April, 2005 ThinQ Innovation 8
P300 Spelling Device – the P300 Event Related Potential
• Known as ‘oddball’ or ‘surprise’ paradigm• Inherent
300ms
8-40 uV avg. deflection
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April, 2005 ThinQ Innovation 9
P300 Spelling Device – the System• Non-invasive
•Inherent Response
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April, 2005 ThinQ Innovation 10
P300 Speller Terminology Epoch = One flash of any row or column Trial = 1 complete set of epochs - all rows and
columns Symbol = Alphanumeric characters or pictures
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April, 2005 ThinQ Innovation 11
BCI Competition 2003
• Provided pre-collected data for competition
• P300 Spelling Paradigm:
-Winners included Kaper et al.
-Used Support Vector Machines
-Achieved high transfer rate with real-time implementation possibilities
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April, 2005 ThinQ Innovation 12
System Operation Steps
– Training (approximate 1hr)• Provide visual stimuli (flashing of rows/columns)• Record data with known classification label• Run data through pattern recognition algorithm (SVM)• Create customized models for each individual
– Spelling• Load customized model for individual• Provide visual stimuli (flashing of rows/columns)• Record data with unknown classification label• Run data through SVM classifier• Sum up decision values• Feedback most probable letter
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April, 2005 ThinQ Innovation 13
Display Flexible matrix size Flexible matrix contents
– Alphanumeric Characters– Words– Symbols
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April, 2005 ThinQ Innovation 14
Display cont… Random and exhaustive flashing of all of the rows
and columns on display Flashing cycle: 300ms
– 100ms intensification period– 200ms de-intensification period
10 second rest period at the end of each symbol
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April, 2005 ThinQ Innovation 15
Data Collection Collect data from DAQ sampled at 240Hz 600ms after intensification Buffer overlap Flexible data collection delay Flexible data recording time
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April, 2005 ThinQ Innovation 16
Data Collection – cont. 10 channels collected simultaneously Data from each channel concatenated together Data stored into program memory Collected until end of a symbol
– Converted to array– Memory cleared for next symbol
System is timing critical
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April, 2005 ThinQ Innovation 17
Timing Issue Purpose
– Process within 300ms window
Bottleneck– Online SVM processing
• Old design = 340ms/Epoch• New design = 17.67ms/Epoch
Requirement– Pentium4 or equivalent is sufficient
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April, 2005 ThinQ Innovation 18
Matlab Interface Why we use Matlab? VB–Matlab interface
using APIs Common functions
– Pass matrix array to Matlab workspace
– Get matrix array from Matlab workspace
– Execute command line or script
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April, 2005 ThinQ Innovation 19
Support Vector Machines Pattern recognition Algorithm SVM used for:
– Creating models for different individuals (train)– Getting discriminant scores (spelling)
Detailed information covered later
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April, 2005 ThinQ Innovation 20
Score Matrix
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 0.000 0.0008 1.100 0.000 0.000 0.000 0.000 0.0009 0.000 0.000 0.000 0.000 0.000 0.000
10 0.000 0.000 0.000 0.000 0.000 0.00011 0.000 0.000 0.000 0.000 0.000 0.00012 0.000 0.000 0.000 0.000 0.000 0.000
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 0.000 0.0008 1.100 1.100 0.000 0.000 0.000 0.0009 0.000 0.000 0.000 0.000 0.000 0.000
10 0.000 0.000 0.000 0.000 0.000 0.00011 0.000 0.000 0.000 0.000 0.000 0.00012 0.000 0.000 0.000 0.000 0.000 0.000
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 0.000 0.0008 1.100 1.100 1.100 0.000 0.000 0.0009 0.000 0.000 0.000 0.000 0.000 0.000
10 0.000 0.000 0.000 0.000 0.000 0.00011 0.000 0.000 0.000 0.000 0.000 0.00012 0.000 0.000 0.000 0.000 0.000 0.000
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 0.000 0.0008 1.100 1.100 1.100 1.100 0.000 0.0009 0.000 0.000 0.000 0.000 0.000 0.000
10 0.000 0.000 0.000 0.000 0.000 0.00011 0.000 0.000 0.000 0.000 0.000 0.00012 0.000 0.000 0.000 0.000 0.000 0.000
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 0.000 0.0008 1.100 1.100 1.100 1.100 1.100 0.0009 0.000 0.000 0.000 0.000 0.000 0.000
10 0.000 0.000 0.000 0.000 0.000 0.00011 0.000 0.000 0.000 0.000 0.000 0.00012 0.000 0.000 0.000 0.000 0.000 0.000
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 0.000 0.0008 1.100 1.100 1.100 1.100 1.100 1.1009 0.000 0.000 0.000 0.000 0.000 0.000
10 0.000 0.000 0.000 0.000 0.000 0.00011 0.000 0.000 0.000 0.000 0.000 0.00012 0.000 0.000 0.000 0.000 0.000 0.000
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 2.011 0.0008 1.100 1.100 1.100 1.100 3.111 1.1009 0.000 0.000 0.000 0.000 2.011 0.000
10 0.000 0.000 0.000 0.000 2.011 0.00011 0.000 0.000 0.000 0.000 2.011 0.00012 0.000 0.000 0.000 0.000 2.011 0.000
Coordinate Index 1 2 3 4 5 6
7 0.000 0.000 0.000 0.000 2.011 0.0008 1.100 1.100 1.100 1.100 3.111 1.1009 0.000 0.000 0.000 0.000 2.011 0.000
10 0.000 0.000 0.000 0.000 2.011 0.00011 -2.500 -2.500 -2.500 -2.500 -0.489 -2.50012 0.000 0.000 0.000 0.000 2.011 0.000
Coordinate Index 1 2 3 4 5 6
7 -8.236 -6.458 -4.596 -1.987 -0.234 -0.2398 -9.300 -8.985 -6.235 -1.256 13.234 -0.8729 -9.632 -7.372 -6.999 -0.945 -1.300 -2.386
10 -8.123 -7.458 -4.563 -2.397 -2.011 -3.02411 -7.985 -8.001 -5.335 -3.598 -3.501 -5.07512 -9.234 -9.021 -6.346 -4.871 2.011 -5.897
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April, 2005 ThinQ Innovation 21
Word Prediction Idea: predict intended words based on previous
spelling. Similar to cellular phone ‘smart text’ Extract top ranked words
– SQL for fast searching– Dynamic database
Selection updated on
the bottom of the display Words chosen same way
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April, 2005 ThinQ Innovation 22
System Design Modular Design Approach
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April, 2005 ThinQ Innovation 23
What is SVM?
Developed by Vapnik in 1992 at Bell Labs Broad applications Based on concept of ‘learn from examples’ Key concepts:
– Linear Decision Boundary with Margin– Nonlinear feature transformation
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April, 2005 ThinQ Innovation 24
Basic Concept
{x1, ..., xn} be our training data set
yi {1,-1} be the class label of xi then,
Find a decision boundary Make a decision on disjoint test data
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April, 2005 ThinQ Innovation 25
Decision Boundary (linear)
Infinite possibility
Class -1
Class 1
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April, 2005 ThinQ Innovation 26
Bad Decision Boundary
Class -1
Class 1
Class -1
Class 1
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April, 2005 ThinQ Innovation 27
Good Decision Boundary
Class -1
Class 1
m Want to maximize m Boundary found using
constrained optimization problem
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April, 2005 ThinQ Innovation 28
Optimization Problem
Optimization Problem
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April, 2005 ThinQ Innovation 29
After Training xi’s on the decision boundary are called
SUPPORT VECTORS Support vectors and b defines the
decision boundary
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April, 2005 ThinQ Innovation 30
Geometrical Interpretation
6=1.4
Class -1
Class 1
1=0.8
2=0
3=0
4=0
5=07=0
8=0.6
9=0
10=0
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April, 2005 ThinQ Innovation 31
Non-separable Samples
Use of Soft Margin Separation Kernel Transformation
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April, 2005 ThinQ Innovation 32
Soft Margin Separation
Class -1
Class 1
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April, 2005 ThinQ Innovation 33
Soft Margin Separation
Idea: simultaneous maximization of margin and minimization of training error
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April, 2005 ThinQ Innovation 34
Nonlinear Samples Some Samples are inherently nonlinear in
input space
No linear boundary is sufficiently accurate
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April, 2005 ThinQ Innovation 35
Solution?
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April, 2005 ThinQ Innovation 36
Kernel Transformation Idea: map input space into feature space
such that samples become linearly separable
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April, 2005 ThinQ Innovation 37
Gaussian Kernel
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April, 2005 ThinQ Innovation 38
SVM Implementation Matlab interface to libsvm Kernel: RBF with = 6.6799e-4 C parameter: 20.007
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April, 2005 ThinQ Innovation 39
SVM Implementation
Average Method (61.538%) Multi-Model Method (65.22%) Concatenation Method (82.418%) Weighted Concatenation Method
(max. 86.264%)
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April, 2005 ThinQ Innovation 40
Possible Improvements Weighted concatenation method
Customized Kernel Parameters
Weighted Concatenation Method
0.50x0.75x
1.25x
1.50x 1.75x 2.00x5x
78
80
82
84
86
88
weighting at (Pz, PO7, PO8 sites)
Accu
racy (
%)
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April, 2005 ThinQ Innovation 41
Measure of Performance Bit Rate
– N: number of available symbols – p: prediction accuracy– t: number of seconds taken to choose one
symbol Letters per minute
1
1log1loglog
60222 N
pp)(+pp+N
t
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April, 2005 ThinQ Innovation 42
Cont… Resulting Transfer Rates
– Without using dictionary
– With using dictionary
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April, 2005 ThinQ Innovation 43
More Accurate Measure Resulting Transfer Rates
– Without using dictionary
– With using dictionary
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April, 2005 ThinQ Innovation 44
Cont… Mechanism
– Receives a chosen letter from control module– Appends the letter to current letters in the word– Searches SQL database– Return list of most probable target words based
on ranking
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April, 2005 ThinQ Innovation 45
Result Analysis
1. Accuracy across subjects
2. Accuracy over time, same subject
3. Accuracy over number of trials
4. Accuracy versus model size
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April, 2005 ThinQ Innovation 46
Accuracy Across Subjects
Date Subject # of Trials Accuracy
(letters)
Percentage
March 26 Jack 15 12/14 86%
March 28 Min 15 12/21 57%
March 30 Brian 15 19/19 100%
March 31st Jyh-Liang 15 26/26 100%
April 2 Lucky 15 25/28 89%
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April, 2005 ThinQ Innovation 47
Accuracy Across Subjects
Date Subject # of Trials Accuracy
(letters)
Percentage
March 31st Jyh-Liang 3 13/13 100%
March 31st Jyh-Liang 2 18/20 90%
March 31st Jyh-Liang 1 10/21 48%
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April, 2005 ThinQ Innovation 48
Accuracy Over Time, Same Subject Subject: Jack
Date Time Change
from Model Made
# of Trials Accuracy
(letters)
Percentage
March 26 0 15 12/14 86%
March 28 2 days 15 19/19 100%
March 31 5 days 15 19/22 86%
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April, 2005 ThinQ Innovation 49
Accuracy Over Number of Trials Subject: Jyh-Liang
Accuracy Vs Number of Trials
0%
20%
40%
60%
80%
100%
120%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Trials
Acc
ura
cy
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April, 2005 ThinQ Innovation 50
Accuracy Versus Model Size
Accuracy Vs Model Size [5 Trials]
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
6.00 10.00 14.00 21.00 25.00 30.00
Size of Models (# of Symbols)
Ac
cu
rac
y (
%)
Subject: Jyh-Liang
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April, 2005 ThinQ Innovation 51
Questions?