Structural EEG analysis: an explorative study.

A method is described to detect (subtle) changes in an EEG (electroencephalogram) by means of a Markovian modeling approach. This method, termed structural EEG analysis, treats the non-stationary EEG as a sequence of a finite number of short elementary patterns. Subtle changes in an EEG may be detected by studying the transition probabilities between the different patterns. By viewing the patterns as states in a Markov chain, a representation of the EEG structure based on a state transition probability matrix emerges. Various techniques to estimate the state transition probability matrices have been investigated. A number of experiments were performed with artificially generated data to determine the data length required to obtain a reliable estimate of the transition matrices. It appeared that a data length of approximately five to eight times the number of entries in the matrices is needed to accurately estimate the matrices. It was determined that the data length required to reliably estimate the transition probability matrix is dependent on the number of states and the number of non-zero entries of the matrix. Also, the data length appears independent of the values of the probabilities. The structural analysis approach was applied to actual EEG data, recorded from normal volunteers and epileptic subjects. It was demonstrated that visually confirmable changes in the EEG could be detected by the structural analysis method more accurately than by a more conventional approach.