Classifying Single Trial EEG: Towards Brain Computer Interfacing

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).

Authors

• Benjamin Blankertz
• Gabriel Curio
• Klaus-Robert Müller

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