EEG correlates of cerebral engagement in reading tasks.

This study evaluated the utility of electroencephalographic (EEG) measures as indices of regional cerebral engagement activation during reading in neurologically intact adult volunteers. EEG was recorded from 16 scalp locations as participants performed four visual detection tasks designed to tap into increasingly more complex operations regularly involved in reading, namely visual-spatial, orthographic, phonological, and semantic. EEG records were quantified using power spectrum measures in four frequency bands (delta, theta, alpha, beta1, and beta2), in addition to a non-linear estimate of signal complexity (prediction error). Results showed that (1) changes in spectral power between pairs of reading tasks, and (2) regional variations in EEG measures for each task, were restricted to signals recorded over the left hemisphere. These findings are in agreement with knowledge regarding left hemisphere involvement in higher level component processes of reading.

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.

Electroencephalographic signal analysis and desynchronization effect caused by two differing mental arithmetic skills.

Using several electroencephalographic signal analysis methods, it is possible to detect activated cortical areas during cognitive processes. These methods are of interest for neuropsychological studies and in an attempt to develop neurophysiological methods that can be used in the clinic. We used two distinct methods to study two different mental arithmetic tasks. Our purpose was to test the hypothesis that there are different desynchronization effects during the two distinct cognitive processes and to compare the two methods used. The first method concerned EEG signal band reactivity changes. The bands were obtained using spectral analysis. The second method is a new one for estimation of the whole EEG signal complexity (ASE = Acceleration Spectrum Entropy). We estimated the EEG signal bands and the ASE changes during two arithmetical tasks in 24 subjects and compared them with the values of the resting state. The ASE method and the EEG band reactivity method could distinguish between the resting condition and the tasks thus demonstrating the usefulness of the ASE method to study cognitive processes. Furthermore, the two tasks affected differently the power spectrum values of the delta, theta and the alpha bands thus indicating the involvement of different brain mechanisms.