Abstract
The study proposes a novel brain–computer interface scheme for the next frontier of telemedicine in human–computer interaction, where the goal is to improve the interactions between users and computers in telemedicine. The system consists of discriminative area selection, feature extraction and classification. Discriminative area selection is proposed to obtain the optimal discriminative area, which can decrease the time length of event-related area to achieve more efficient computation and higher accuracy. A fuzzy Hopfield neural network is used to classify the features extracted by means of wavelet-fractal approach. Experimental results show that the proposed system is robust and performs better than several previous methods. It is also suggested being suitable for the applications of telemedicine in human–computer interaction.
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