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Idle state detection in self-paced Brain-Computer Interfaces

Self-paced Brain-Computer Interfaces (BCIs) are quite a different breed than their cue-paced counterparts. However, most asynchronous BCI paradigms are still based on single-trial classification techniques which are difficult to extend to selfpaced operation. We develop a method for extracting discriminative amplitude modulation features and a processing pipeline that separately deals with impulse and normally distributed noise. A Finite Impulse Response Wiener filter is employed to learn a linear mapping from feature space to desired output. The proposed methodology was validated on the selfpaced dataset 1 of the BCI Competition IV where it obtained an average decrease of 6% in mean squared error compared to the winning algorithm of the competition.