Significant EEG features involved in mathematical reasoning: evidence from wavelet analysis.

Using electroencephalographic (EEG) signals and a novel methodology based on wavelet measures in the time-scale domain, we evaluated cortex reactions during mathematical thinking. Our purpose was to extract more precise information from the cortex reactions during this cognitive task. Initially, the brain areas (lobes) of significant activation during the task are extracted using time-averaged wavelet power spectrum estimation. Then, a refinement step makes use of statistical significance-based criteria for comparing wavelet power spectra between the task and the rest condition. EEG signals are recorded from 15 young normal volunteers using 30 scalp electrodes as participants performed one difficult arithmetic task and the results are compared with a rest situation. The results are in accordance with similar previous studies, showing activations of frontal and central regions. Compared with the alternative spectral-based techniques, the method we propose achieves higher task discrimination on the same dataset and provides additional detail-signal information to evaluate cortical reactivity during local cortical activation.