Evaluation of time-domain features for motor imagery movements using FCM and SVM
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Date
2012-05-30
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Brain–Machine Interface is a direct communication
pathway between brain and an external electronic device. BMIs
aim to translate brain activities into control commands. To
design a system that translates brain waves and its activities to
desired commands, motor imagery tasks classification is the core
part. Classification accuracy not only depends on how capable
the classifier is but also it is about the input data. Feature
extraction is to highlight the properties of signal that make it
distinct from the signal of the other mental tasks. Performance of
BMIs directly depends on the effectiveness of the feature
extraction and classification algorithms. If a feature provides
large interclass difference for different classes, the applied
classifier exhibits a better performance.
In order to attain less computational complexity, five timedomain procedure, namely: Mean Absolute Value, Maximum
peak value, Simple Square Integral, Willison Amplitude, and
Waveform Length are used for feature extraction of EEG signals.
Two classifiers are applied to assess the performance of each
feature-subject. SVM with polynomial kernel is one of the
applied nonlinear classifier and supervised FCM is the other one.
The performance of each feature for input data are evaluated
with both classifiers and classification accuracy is the considered
common comparison parameter.
Description
Keywords
Feature extraction, Motor imagery, Brain-Machine Interface, Support Vector Machine, Fuzzy C-Means
Citation
Khorshidtalab, A., Salami, M. J. E., & Hamedi, M. (2012, May). Evaluation of time-domain features for motor imagery movements using FCM and SVM. In 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE) (pp. 17-22). IEEE.