Nonlinear Analysis of Cognitive and Motor-related EEG Signals

After the recent development of the theory of nonlinear dynamical systems and deterministic chaos and the introduction of one, in theory, simple method for computing the phase space, many researchers started to analyze electroencephalographic (EEG) signals in terms of nonlinear dynamics, with the belief that new information not accessible by linear analysis could be obtained. However, after an initial enthusiasm, many results obtained with short-length and noisy data have come under question. After a short theoretical introduction of the field and the explanation of a practical method to compute reasonable embedding parameters, we present an overview of the results obtained and the problems encountered by the application of nonlinear analysis to cognitive functions.