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• W2078480419 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." @default.
• W2078480419 created "2016-06-24" @default.
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• W2078480419 date "2000-06-01" @default.
• W2078480419 modified "2023-10-03" @default.
• W2078480419 title "EEG-based discrimination between imagination of left and right hand movements using adaptive gaussian representation" @default.
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• W2078480419 doi "https://doi.org/10.1016/s1350-4533(00)00051-5" @default.
• W2078480419 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/11121767" @default.
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