Motion Intention classification based on temporal and frequency features

To develop prosthesis controlled by spontaneous brain signal, we studied the motion intention underlying EEG activity with the imagination of right hand and left hand. During hand motor imaginary, EEG activities are collected by 64 electrodes from healthy adults. The temporal and frequency characteristics associated with the imagination of movement are explored and combined as the feature vector for the linear discriminant analysis. Our findings show a classification accuracy of 77% for our datasets and 83% for the datasets of BCI Competition 2003.

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