Received: 08 Sep 2001 © Copyright 2001 Accepted: Pending – 1 – http://www.csu.edu.au/ci/sub09/das01/ http://www.csu.edu.au/ci/ Draft Manuscript Please note that this manuscript has yet to be accepted by Complexity International Applicability of Lyapunov Exponent in EEG data analysis Atin Das, Pritha Das, A. B. Roy Department of Mathematics, Jadavpur University, Jadavpur, Calcutta 700 032, India Email: atin_das@yahoo.com Abstract Importance of role of chaos in brain functioning has lead theoretical modelling as well as analysis of electroencephalogram [EEG] data. Two most important chaotic measures are Lyapunov exponent (LE) and dimensional analysis. But the reliability of LE in EEG data analysis has come under question on the basis of the finding that EEG does not represent low dimensional chaos. Here we address this question. We shall at first calculate LE and fractal dimension of EEG data and known Lorenz system. We shall use the surrogating data method to remove nonlinearity of the two datasets. A comparison of values of both the measures for original and its surrogated counterpart is made to find if they can reflect the loss of nonlinearity and hence the loss of information. We find that for EEG data, LE values does not reflect such change, but fractal dimension shows such loss of information. In case of Lorenz data, both the measures reflect the change correctly. So it can be concluded that LEs are not reliable tool in not low dimensional EEG analysis. 1. Introduction Electroencephalogram [EEG] represents the time series that maps the voltage corresponding to neurological activity as a function of time. EEG is an observable property of large fields of real neurons. Since the 1980s, when EEG signals could easily be connected to computers, the technology has become widely used in mapping the brain. The status of EEG, despite for time being eclipsed by that of single neuron recording, has since become a classic area of investigating dynamics at the neurosystem level. Many investigators, for example, Duke et al (1991), has proved that complex dynamical evolutions lead to chaotic regimes. In the last thirty years, experimental observations have pointed out that, in fact, chaotic systems are common in nature. A detail of such system is given in Boccaletti et al. (2000).