Quantitative measure of complexity of EEG signal dynamics

Since electroencephalographic (EEG) signal may be considered chaotic, Nonli near Dynamics and Deterministic Chaos Theory may supply effective quantitat ive descriptors of EEG dynamics and of underlying chaos in the brain. We ha ve used Karhunen-Loeve decomposition of the covariance matrix of the EEG si gnal to analyse EEG signals of 4 healthy subjects, under drug-free conditio n and under the influence of Diazepam. We found that what we call KL-comple xity of the signal differs profoundly for the signals registered in differe nt EEG channels, from about 5-8 for signals in frontal channels up to 40 an d more in occipital ones. But no consistency in the influence of Diazepam a dministration on KL-complexity is observed. We also estimated the embedding dimension of the EEG signals of the same subjects which turned to be betwe en 7 and 11, so endorsing the presumption about existence of low-dimensiona l chaotic attractor. We are sure that nonlinear time series analysis can be used to investigate the dynamics underlying the generation of EEG signal. This approach does not seem practical yet, but deserves further study.