Key result
Support vector machine classification of EEG signals successfully detected a subject's planning to perform a task, yielding results comparable to recent works.
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In this work, EEG signals were classified by support vector machines to detect whether a subject's planning to perform a task or not. Various different kernels were utilized to find the best kernel function and after that, a feature selection process was realized. The results are comparable to the recent works.
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Support vector machines with appropriate kernel functions and feature selection can effectively classify EEG signals to detect task planning.
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Bayram et al. (2013) studied this question. Support vector machine classification of EEG signals successfully detected a subject's planning to perform a task, yielding results comparable to recent works.
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