EEG based foot movement onset detection with the probabilistic classification vector machine
作者： Raheleh Mohammadi , Ali Mahloojifar , Huanhuan Chen , Damien Coyle
DOI: 10.1007/978-3-642-34478-7_44
关键词:
摘要: A critical issue in designing a self-paced brain computer interface (BCI) system is onset detection of the mental task from continuous electroencephalogram (EEG) signal to produce switch. This work shows significant improvement movement based BCI by applying new sparse learning classification algorithm, probabilistic vector machines (PCVMs) classify EEG signal. Constant-Q filters instead constant bandwidth for frequency decomposition are also shown enhance discrimination related patterns associated with idle state. Analysis data recorded seven subjects executing foot using constant-Q and PCVMs statistically 17% (p<0.03) average true positive rate (TPR) 2% reduction false (FPR) compared SVM classifier.
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