Real-time EEG classification via coresets for BCI applications.

A brain-computer interface (BCI) based on the motor imagery (MI) paradigm translates a subject’s motor intention into a control signal by classifying the electroencephalogram (EEG) signals of different tasks. However, most existing systems use either (i) a high-quality algorithm to train the data off-line and run only the classification in real-time since the off-line algorithm is too slow, or (ii) low-quality heuristics that are sufficiently fast for real-time training but introduce relatively large classification error.