Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller
Driven by the progress in the ﬁeld of single-trial analysis of EEG, there is a growing interest in brain computer interfaces (BCIs), i.e., systems that enable human subjects to control a computer only by means of their brain signals. In a pseudo-online simulation our BCI detects upcoming ﬁnger movements in a natural keyboard typing condition and predicts their lat- erality. This can be done on average 100–230 ms before the respective key is actually pressed, i.e., long before the onset of EMG. Our approach is appealing for its short response time and high classiﬁcation accuracy (>96%) in a binary decision where no human training is involved. We compare discriminative classiﬁers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable reg- ularization properties for dealing with high noise cases (inter-trial vari- ablity).