Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles

Among the various frameworks in which EEG signal analysis has been traditionally formulated, the most widely studied is employing power spectrum measures as functions of certain brain pathologies or increased cerebral engagement. Such measures may form signal features capable of characterizing and differentiating the underlying neural activity. The objective of this chapter is to validate the use of wavelets in extracting such features in the time–scale domain and evaluate them in a simulated environment assuming two tasks (control and target) that resemble widely used scenarios of assessing and quantifying complex cognitive functions or pathologies. The motivation for this work stems from the ability of time–frequency features to encapsulate significant power alteration of EEG in time, thus characterizing the brain response in terms of both spectral and temporal activation. In the presented algorithmic scenario, brain areas’ electrodes of significant activation during the target 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 target task and the control condition. The results indicate the ability of the proposed methodological framework to correctly identify and select the most prominent channels in terms of “activity encapsulation,” which are thought to be the most significant ones.