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Abstract:
Driven by the proress 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 laterality. This can
be done on average 100-203 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 regularization properties for dealing with high noise
cases (inter-trial variability).
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