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The input signals of brain-computer interfaces(BCIs)may be either scalp electroencephalogram(EEG)or electrocor- ticogram(ECoG)recorded from subdural electrodes.To make BCIs practical,the classifiers for discriminating different brain states must have the ability of session-to-session transfer.This paper proposes an algorithm for classifying single-trial ECoG during motor imagery of different sessions.Three features,derived from two physiological phenomena,movement-related potentials(MRP)and event-related desynchronization(ERD),and extracted by common spatial subspace decomposition(CSSD)and waveform mean,are combined to per- form classification tasks.The specific signal processing methods utilized are described in detail.The algorithm was successfully applied to Data SetⅠof BCI CompetitionⅢ,and achieved a classification accuracy of 91% on test set.
The input signals of brain-computer interfaces (BCIs) may be either scalp electroencephalogram (EEG) or electrocor- ticogram (ECoG) recorded from subdural electrodes. To make BCIs practical, the classifiers for discriminating different brain states must have the ability of session- to-session transfer.This paper proposes an algorithm for classifying single-trial ECoG during motor imagery of different sessions. Three features derived from two physiological phenomena, movement-related potentials (MRP) and event-related desynchronization (ERD) by common spatial subspace decomposition (CSSD) and waveform mean, are combined to per- form classification tasks. The specific signal processing methods utilized are described in detail. The algorithm was successfully applied to Data Set I of BCI Competition III, and achieved a classification accuracy of 91 % on test set.