摘要：Modern electrophysiological studies in animals show that the spectrum of neural
oscillations encoding relevant information is broader than
previously thought and that many diverse areas are engaged for very simple tasks. However,
EEG-based brain-computer interfaces
(BCI) still employ as control modality relatively
slow brain rhythms or features derived from preselected
frequencies and scalp locations. Here, we describe the
strategy and the algorithms we have developed for the analysis of
electrophysiological data and demonstrate their capacity to
lead to faster accurate decisions based on linear classifiers.
To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor
movement by a paraplegic patient. For this data, we show that although the patient received
extensive training in mu-rhythm control, valuable information about movement imagination is present
on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency
rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including
the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges
based on their discriminative power, the classification rates can be systematically improved with respect to
results published thus far.
