Extracting structure from wake EEG using neural networks.

This paper considers the relevance of nonlinear feature extraction for the analysis of real-world single channel wake EEG sig nals. It is demonstrated that it is feasible to extract structured patterns which possibly reflect the state of mind of the subject. This is exhibited by a clustering and a dynamics in a feature space derived by a dynamical systems approach of projecting the information into the space spanned by the lowest order singular vectors determined from a matrix of delay vectors. An embedding of the signal was obtained in an 11-dimensional Euclidean space indicating a relatively small number of intrinsic degrees of freedom in the data. Feature extraction and clusterings in the signal have been obtained using linear methods (Principal Component projections) and nonlinear approaches (the neural network technique known as 'NEUROSCALE'). Although most of the analysis was performed in an unsupervised manner (without using any task-specific information), a final clustering was demonstrated which used some of the task-related knowledge to obtain more distinct clusters. The interesting aspect was that in both linear and nonlinear methods the characteristic clusters did not align themselves in an order which reflected the time of day of the tasks, or even the type of tasks. Our supposition is that the self-organised clusters are driven by a higher level cognitive state such as the 'attentiveness' of the subject though no data is available to test the hypothesis.