title: EEG-based discrimination between imagination of left and right hand movements using Adaptive Gaussian Representation.
abstract: This article uses the Adaptive Gaussian Representation (AGR) for human electroencephalogram (EEG) feature extraction aiming the discrimination among mental tasks to be used in a brain computer interface (BCI). It does not focus on the AGR time-frequency representation, but rather on their projection coefficients. Ten volunteers were asked to imagine either right or left hand movement, according to a proper visual stimulus. The features of the resulting EEG signals were characterised by extracting AGR coefficients. Classification was carried out using a Multilayer perceptron (MLP) trained with the classical backpropagation algorithm. Overall results show that AGR coefficients representation is able to reveal a significant EEG discrimination between imagination of right and left hand movement with a mean classification performance of 91%+/-5.8% achieved for female subjects and 87%+/-5.0% achieved for male subjects.
authors: Communication and Signal Processing Laboratory, Department of Telecommunications and Control, Escola Politécnica, São Paulo University, São Paulo, SP, Brazil. ernane@lcs.poli.usp.br
authors: E J
authors: EJ
authors: Costa
authors: E F
authors: EF
authors: Cabral
mesh_terms: D000328:Adult; D004569:Electroencephalography; D005260:Female; D006225:Hand; D006801:Humans; D007092:Imagination; D008297:Male; D009068:Movement; D016571:Neural Networks, Computer; D016011:Normal Distribution