Synchronization analysis of short EEG data through time-evolving relative wavelet entropy and IAPS affective visual stimuli

Abstract

Synchronization analysis of EEG data has been so far performed by means of coherence functions or non-linear similarity quantifications. However, linear methods fail to provide information about the entire frequency spectrum or the direction of the interaction, while non-linear estimates require time-consuming computations, difficult parameter tuning and huge amounts of data. This paper, aims to overcome the above limitations by investigating the feasibility of using the time-evolving Relative Wavelet Entropy (RWE) for the quantification of the similarity degree between homologous electrodes on either hemisphere. Emotional stimuli selected from the International Affective Picture Stimuli (IAPS) collection are employed in order to induce neurophysiological responses. The methodological framework involves the analysis of the EEG data in time intervals of 128 ms duration. The results showing increased similarity during early, mid and late emotional processing indicate the method's robustness providing hope for the dynamic characterization of the cooperative brain activity during cognitive functioning.