USEFULNESS OF NONLINEAR EEG-ANALYSIS

Spectral analysis methods are useful for the evaluation of EEG signals . Nevertheless, they refer only to the frequency domain and ignore any potentially interesting phase information. Analytical methods based u pon the theory of nonlinear dynamics provides this and additional info rmation. We used both methods to evaluate the EEG signals of volunteer s performing two distinct mental arithmetic tasks. We extracted the po wer spectrum, the coherence and nonlinear parameters (dimension, the f irst Lyapunov exponent, the Kolmogorov entropy, the mutual dimension a nd the dimensions based upon spatial embedding of the original data as well as their surrogates). We found that 1) the spatial embedding dim ension differed from that of the surrogates, indicating nonlinearity, 2) there were differences between the two arithmetic tasks, and 3) the spectral and nonlinear methods differ in terms of the information the y provide. Our results indicate that nonlinear analysis methods can be useful despite the fact that they are still at an early stage of deve lopment.