Abstract:
:The extraction of distinctly separable features from electroencephalogram (EEG) is one of the main challenges in designing a brain-computer interface (BCI). Existing feature extraction techniques for a BCI are mostly developed based on traditional signal processing techniques assuming that the signal is Gaussian and has linear characteristics. But the motor imagery (MI)-related EEG signals are highly non-Gaussian, non-stationary and have nonlinear dynamic characteristics. This paper proposes an advanced, robust but simple feature extraction technique for a MI-related BCI. The technique uses one of the higher order statistics methods, the bispectrum, and extracts the features of nonlinear interactions over several frequency components in MI-related EEG signals. Along with a linear discriminant analysis classifier, the proposed technique has been used to design an MI-based BCI. Three performance measures, classification accuracy, mutual information and Cohen's kappa have been evaluated and compared with a BCI using a contemporary power spectral density-based feature extraction technique. It is observed that the proposed technique extracts nearly recording-session-independent distinct features resulting in significantly much higher and consistent MI task detection accuracy and Cohen's kappa. It is therefore concluded that the bispectrum-based feature extraction is a promising technique for detecting different brain states.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
Shahid S,Prasad Gdoi
10.1088/1741-2560/8/2/025014subject
Has Abstractpub_date
2011-04-01 00:00:00pages
025014issue
2eissn
1741-2560issn
1741-2552pii
S1741-2560(11)74667-5journal_volume
8pub_type
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