eeg features in mental tasks recognition and...
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EEG Features in Mental Tasks Recognition and Neurofeedback
Ph.D. Candidate: Wang Qiang
Supervisor: Asst. Prof. Olga Sourina
Co-Supervisor: Assoc. Prof. Vladimir V. Kulish
Division of Information Engineering
School of Electrical and Electronic Engineering
Nanyang Technological University
Institute for Media Innovation
Nanyang Technological University
1
Outline
Background & Motivation & Objective
Demos & Publication List
Proposed Algorithms
2
Conclusion & Future Works
EEG: EEG provides wonderful tools for brain state monitoring.
– High temporal resolution.
– Tremendous algorithms are available for time series.
– Successful medical applications.
Neurofeedback: Neurofeedback systems provide visual/audio feedback according to
EEG signal. It is useful for brain training. – Use neurofeedback to enhance the work performance.
– Use neurofeedback to treat ADHD patients.
Motivation
3
This project is inter-disciplinary:
biosignals medical application serious game
pattern recognition cognitive informatics psychology
Research Objectives:
• Design an experiment protocol for mental tasks recognition.
• Study nonlinear model and propose effective EEG features for mental tasks recognition.
• Propose faster, more accurate algorithms with less EEG channels for mental tasks recognition.
• Propose neurofeedback strategies.
• Design and implement 2D and 3D neurofeedback games.
• Develop a protocol to use neurofeedback game for psychological disorder treatment and optimum concentration level searching.
• Use proposed concentration level recognition techniques to provide a feedback loop in e-learning system.
Research Objective
4
Outline
Background & Motivation & Objective
Demos & Publication List
Proposed Algorithms
5
Conclusion & Future Works
Relative power training in EEG based neurofeedback.
• Theta/Beta training1. Increase theta band power.
Decrease beta band power.
• Active alpha training2. Increase alpha band power.
Decrease EMG power.
1. T. M. Sokhadze, et al., "EEG biofeedback as a treatment for substance use disorders: Review,
rating of efficacy, and recommendations for further research," Applied Psychophysiology
Biofeedback, vol. 33, pp. 1-28, 2008.
2. S. Hanslmayr, et al., "Increasing individual upper alpha power by neurofeedback improves
cognitive performance in human subjects," Applied Psychophysiology Biofeedback, vol. 30, pp. 1-
10, 2005.
Related Works
6
A well-known EEG database for mental tasks classification
recorded by Zak Keirn1 is available.
• Seven subjects participated the experiment for two session.
• In each session, subjects performed 5 different mental tasks
for 5 trials.
Relax Counting Letter composition Multiplication Rotation
1. Z. Keirn,“ Alternative modes of communication between man and machine,” Master’s thesis,
Electrical Engineering Department, Purdue University,USA,1998.
EEG Database for Mental Tasks
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N. Liang et. al.1 processed the mental tasks EEG database in 2006.
• Autoregressive features were used.
• Different classifiers were compared, multi-class SVM classifier
can achieve the best accuracy.
• With multi-class SVM classifier, 52.07% accuracy were reported
for multi-class classification.
1. N. Liang, P. Saratchandran, G. Huang, and N. Sundararajan, “Classification of mental tasks from eeg signals using
extreme learning machine,” International Journal of Neural Systems, vol. 16, no. 1, pp. 29–38, 2006.
Related Works
8
EEG data were processed according to the following procedure.
EEG Signal Processing
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EEG Signal Segmentation
Ocular Artifact Removal
Feature Extraction
Feature Selection
Classification
EEG signals were divided into segments with 512 samples
(overlapping with 480 samples).
EEG Signal Segmentation
10
128
512 samples
Segment 1 Segment 2 ….
32
Ocular artifacts were detected by applying with a fixed-weight
leakage normalized stochastic least mean fourth algorithm1 on
EOG channel.
Segments contains OAs were discarded.
1. P.Celka, B.Boashash, and P.Colditz, “Preprocessing and time-frequency analysis of new born eeg
seizures,” IEEE Engineering in Medicine and Biology Magazine,vol.20, no.5, pp.30–39, 2001.
Ocular Artifact Removal
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Six group of features were extracted from each clean segment.
Feature Extraction
12
Feature Type No. of feature Time cost (ms)
Relative Power (PSD) 5 1
Autoregressive (AR) coefficient 6 70
Higher Order Crossing (HOC) 16 240
Generalized Higuchi Fractal
Dimension Spectrum (GHFDS)
2 620
Entropy 10 1500
Statistical 6 1
Generalized fractal dimension spectrum.
Feature Extraction
13
To speed up multi-class svm evaluation, we applied feature
selection method before classification. Following features
selection schemes were considerate and compared.
Random Forests (RF) scheme could achieve the best performance.
Feature Selection
14
Multi-class SVMs were used as classifier.
RBF kernel was applied and C-gamma parameters were selected
with grid search procedure.
Classification
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Mental tasks classification results when different features were
used.
Statistical features could achieve better accuracy than AR features which were
used in N. Zhang’s research1. In their paper, the accuracy is 52.07%.
Combine all features could enhance the performance.
1. N. Liang, P. Saratchandran, G. Huang, and N. Sundararajan, “Classification of mental tasks from eeg signals using
extreme learning machine,” International Journal of Neural Systems, vol. 16, no. 1, pp. 29–38, 2006.
Classification Result
16
Benefits of feature selection.
Classification Result
17
Benefits of feature selection.
Classification Result
18
Experiment Setup:
• EEG recording device
– 14-channels,
– Sampling frequency: 128 Hz,
– A/D resolution: 16-bit.
• PC for processing data
– CPU: Intel Core 2 Quad Q9400 (2.66 Hz * 4),
– RAM: DDR3 3.25 GB,
• EEG processing software
– EEG recording : Emotiv Testbench,
– EEG processing: Numpy.
• Subjects
– 10 subjects.
Arithmetic Task Experiment
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Data Acquisition Protocol:
• Session 1
– Relaxation Session (Relax, no task to fulfill)
• Session 2
– Arithmetic Session (Working on 3-digit arithmetic problems)
Arithmetic Task Experiment
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Comparison between different type of EEG features.
Classification Result
21
Comparison between different type of EEG features.
Classification Result
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EEG channel rank.
Classification Result
23
EEG channel rank.
Classification Result
24
Outline
Background & Motivation & Objective
Demos & Publication List
Proposed Algorithms
25
Conclusion & Future Works
A well-known EEG database for mental tasks recognition was also used.
Arithmetic task experiment was also designed and carried out to collect the
labeled EEG data.
Proposed and implemented Fractal Dimension Model Study. Generalized
Higuchi Fractal Dimension Spectrum.
Proposed and implemented Mental tasks recognition algorithms.
Statistical features could achieve the best accuracy (55.23% ).
Combine all features could enhance the accuracy (59.82%).
With random forests feature selection method, the no. of features used in
classification can be reduced to 77 and the classification can be maintained
(60.41%).
(F8, F3, AF3, O2) channels are important for arithmetic task classification.
Proposed and implemented neurofeedback games based on novel EEG
features.
Conclusion
26
• Parallelize the feature extraction step with MapReduce
Model.
• Develop real-time mental tasks recognition application
based on Hadoop framework.
• Design neurofeedback novel algorithm and compare the
working performance enhancement with alpha train
neurofeedback.
Future Works
27
Outline
Background & Motivation & Objective
Demos & Publication List
Proposed Algorithms
28
Conclusion & Future Works
Real-time EEG monitoring tool.
Blooby Demo
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Demonstrate EEG Properties on 3D models.
Support real-time mode and playback mode.
Support interactive operation.
3 type of indicators.
Neurofeedback games.
Neurofeedback Demos
30
Book Section: O. Sourina, Q. Wang, Y. Liu, M. K. Nguyen, EEG-enabled Human-Computer Interaction and
Applications, in Towards Practical Brain-Computer Interfaces, B. Allison, etc., Springer, in
press, 2011
Journal Papers:
Sourina, O., Wang, Q., Liu, Y., , Nguyen, M. K., Fractal-based Brain State Recognition from
EEG in Human Computer Interaction, Communications in Computer and Information Science,
In Press
Wang, Q., Sourina, O., Nguyen, M. K., Fractal dimension based neurofeedback in serious
games, Visual Computer, Vol.27, No. 4, pp. 299-309
Sourina, O., Wang, Q., Nguyen, M. K., EEG-based "Serious" games and monitoring tools for
pain management, Studies in Health Technology and Informatics, Vol.163, pp. 606-610
Sourina, O., Liu, Y., Wang, Q., Nguyen, M. K., EEG-based personalized digital experience,
Lecture Notes in Computer Science , Vol.6766, pp. 591-599
Conference Papers: Wang, Q., Sourina, O., Nguyen, M. K., EEG-based "Serious" Games Design for Medical
Applications, Proc. 2010 Int. Conf. on Cyberworlds, 2010, pp. 270-276
Sourina, O., Wang, Q., Liu, Y., , Nguyen, M. K., A real-time fractal-based brain state
recognition from EEG and its applicationse, Proc. 2011 Biosignals , 2011, pp. 82-90
Publication List
31
Q & A
32
Generalized fractal dimension spectrum.
Feature Extraction
33
Statistical features1.
Feature Extraction
34
1. R. Picard, E. Vyzas, and J. Healey, “Toward machine emotional intelligence: Analysis of affective
physiological state,” IEEE Transactionson Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp.
1175–1191, 2001.
Relative Power features1.
Feature Extraction
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1. S. Sanei and J. A. Chambers, EEG Signal Processing. San Francisco: WILEY, 2007.
Autoregressive coefficients.
AR(6) model is used to model EEG segments.
Feature Extraction
36
1. N. Liang, P. Saratchandran, G. Huang, and N. Sundararajan, “Classification of mental tasks from
eeg signals using extreme learning machine,” International Journal of Neural Systems, vol. 16, no.
1, pp. 29–38, 2006.
The q order difference operator is defined as:
The crossing number is summarize as follow:
Higher order crossing.
Difference operator is defined as:
Feature Extraction
37
1. S. He and B. Kedem, “Higher order crossings spectral analysis of an almost periodic random sequence in
noise,” IEEE Transactionson Information Theory, vol. 35, no. 2, pp. 360–370, 1989.
Entropy.
Entropy could be used as another important quantification feature in nonlinear
dynamical analysis of time series which is related to the rate of information
production.
We calculated three types of entropy which could be applied to short and noisy
time series:
approximate entropy1
sample entropy1
SVD entropy2
Feature Extraction
38
1. J. Richman and J. Moorman, “Physiological time-series analysis using approximate and sample entropy,”
American Journal of Physiology Heart and Circulatory Physiology, vol.278, no.647-6, pp.H2039–H2049,
2000.
2. S. Faul, G. Boylan, S. Connolly, W. Marnane, and G. Lightbody, “Chaos theory analysis of the new born
eeg-is it worth the wait?”, pp. 381–386, 2005.
Random forests.
Random Forests (RF) method proposed by Breiman1 was used as the supervised feature
selection scheme.
This method could deal with the situation when there are many more features than
observations. This method also reduces the risk of overfitting2.
Feature Selection
39
1. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
2. S. Diaz-Uriarte and R. A. deAndres, “Gene selection and classification of microarray data using random
forest,” BMC Bioinformatics, vol. 7, no. 3, 2006.
Other features selection schemes.
LASSO1
Stability selection2
F-score3
All these scheme is implemented by scikit-learning python library4.
Feature Selection
40
1. R. Tibshirani, “Regression shrinkage and selection via the lasso: A retrospective,” Journal of the Royal
Statistical Society. SeriesB:Statistical Methodology, vol. 73, no.3, pp.273–282, 2011.
2. N. Meinshausen and P. Buhlmann, “Stability selection,” Journal of the Royal Statistical Society.
SeriesB:Statistical Methodology, vol.72, no.4, pp.417–473, 2010.
3. Y. Chen and C.Lin, “Combining svms with various feature selection strategies,” Studies in Fuzziness and
Soft Computing, vol. 207, pp.315–324,2006.
4. F. Pedregosa, G. Varoquaux, A. Gramfort, V.Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.
Weiss, V. Dubourg, J.Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, “Scikit-learn:
Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
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