The Advanced Complexity Analysis of Electroencephalography (EEG) Data Using Tsallis Entropy

Abstract

This paper introduces a novel application of Tsallis entropy for complexity analysis in electroencephalography (EEG) data. Tsallis entropy, a generalization of Shannon entropy, is employed to uncover hidden structures and distinguish varying complexity levels in EEG signals. By leveraging this framework on publicly available EEG datasets, the study demonstrates that Tsallis entropy is highly effective in categorizing brain activity patterns across different levels of complexity. The results highlight the method’s potential for clinical and experimental neurodata analysis.