Alternate Modes of Human-Computer Interaction: EEG Recognition with Neural Networks

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This is the first year of a three year continuing effort. This research is to study the feasibility of human-computer interaction through the use of on-line recognition of electroencephalogram (EEG) patterns. A system that can identify, on-line, which of several mental tasks a person is performing could provide an alphabet with which a severely disabled person can compose commands to devices like a wheel chair. Most prior research has focused on recognition accuracy rather than on fast, real-time responses. The goals of this research are to characterize the limitations of current EEG recognition methods, to increase the accuracy and decrease the computation time of EEG recognition by use of a neural network approach, and to develop a practical real-time method. The performance of conventional methods and of neural network learning methods designed for high- dimensional data will be compared. Evaluations of feature and classifier characteristics will be based on classification accuracy, robustness and computation speed. This project has relevance to the field of pattern recognition in general, and to the neural network and human-computer interaction fields in particular.