Use of non-linear EEG analysis to study abnormal brain dynamics in deaf human subjects.

We compared the cortical dynamics of deaf subjects to those of control subjects at rest with eyes closed and during reading with the help of a non-linear prediction statistic. This method is suitable for short-term noisy time series such as electroencephalographic signals. Furthermore, we used surrogate data to test for non-linear dynamics underlying the electroencephalographic time series recorded. Our results indicate that significant non-linearity accompanies cortical activation during reading. This is more diffuse in deaf subjects and could be due to the widespread reorganization of their cerebral cortex. Predictability was lower in deaf subjects at rest, which indicates their increased 'readiness' in the resting condition. Finally, our results indicate that normal and deaf subjects differ significantly in terms of cortical dynamics.

Usefulness of non-linear 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 upon the theory of nonlinear dynamics provides this and additional information. We used both methods to evaluate the EEG signals of volunteers performing two distinct mental arithmetic tasks. We extracted the power spectrum, the coherence and nonlinear parameters (dimension, the first Lyapunov exponent, the Kolmogorov entropy, the mutual dimension and the dimensions based upon spatial embedding of the original data as well as their surrogates). We found that 1) the spatial embedding dimension 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 they provide. Our results indicate that nonlinear analysis methods can be useful despite the fact that they are still at an early stage of development.