EEG-based communication via dynamic neural network models
The overall aim of this research is to develop an EEG-based computer interface. We report on an offline analysis of EEG data recorded from 7 subjects performing two different pairs of cognitive tasks; motor imagery versus a baseline task and motor imagery versus a maths task. For the imagery versus baseline pairing, discrimination was good in three subjects, marginal in two and not possible in the other two. For the imagery versus maths pairing, discrimination was very good in two subjects, good in 4 and marginal in one. The data was analysed using lagged-AR feature vectors and a Bayesian logistic regression classifier with temporal smoothing. Enhanced spectra are shown highlighting differential spectral activity for each task pairing. The results suggest that combinations of different task pairings and dynamic neural network models have the potential to drastically reduce the time it takes for a new user to learn to use an EEG-based computer interface.