Evaluating the effectiveness of time-domain features for motor imagery movements using SVM
Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain wa...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/26891/ http://irep.iium.edu.my/26891/ http://irep.iium.edu.my/26891/1/AidaPaper2012B.pdf |
| Summary: | Motor imagery electroencephalogram signals are the
only bio-signals that enable locked-in patients, who have lost
control over every motor output, to communicate with and
control their surroundings. Brain Machine Interface is
collaboration between a human and machines, which translates
brain waves to desired, understandable commands for a
machine. Classification of motor imagery tasks for BMIs is the
crucial part. Classification accuracy not only depends on how
accurate and robust the classifier is; it is also about data. For well
separated data, classifiers such as kernel SVM can handle
classification and deliver acceptable results. If a feature provides
large interclass difference for different classes, immunity to
random noise and chaotic behavior of EEG signal is rationally
conformed, which means the applied feature is suitable for
classifying EEG signals. In this work, in order to have less
computational complexity, time-domain algorithms are employed
to motor imagery signals. Extracted features are: Mean Absolute
Value, Maximum peak value, Simple Square Integral, Willison
Amplitude, and Waveform Length. Support Vector Machine
with polynomial kernel is applied for classification of four
different classes of data. The obtained results show that these features have acceptable, distinct values for different these four motor imagery tasks. Maximum classification accuracy belongs to contribution of Willison amplitude as feature and SVM as classifier, with 95.1 percentages accuracy. Where, the lowest is the contribution of Waveform Length and SVM with 31.67 percentages classification accuracy. |
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