Complexity and Information-Based Analysis of the Electroencephalogram (Eeg) Signals in Standing, Walking, and Walking With a Brain–computer Interface

Abstract

In this paper, we analyzed the variations in brain activation between different activities. Since Electroencephalogram (EEG) signals as an indicator of brain activation contain information and have complex structures, we employed complexity and information-based analysis. Specifically, we used fractal theory and Shannon entropy for our analysis. Eight subjects performed three different activities (standing, walking, and walking with a brain–computer interface) while their EEG signals were recorded. Based on the results, the complexity and information content of EEG signals have the greatest and smallest values in walking and standing, respectively. Complexity and information-based analysis can be applied to analyze the activations of other organs in different conditions.

Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains

“…Our approach aligns with current trends in neuroscience and psychology, where ensemble machine-learning methods have shown effectiveness in interpreting complex neural patterns ( Rahman et al, 2022 ; Li et al, 2023 ). Our study is focused on examining a broad range of EEG features, totaling 45 in number, which encompass elements from the time domain ( Al-Fahoum and Al-Fraihat, 2014 ; Zuckerman et al, 2022 , 2023b ), frequency-based analyses ( Mizrahi et al, 2022a , b , 2023a ), and complexity measures ( Sheehan et al, 2018 ; Ramadoss et al, 2022 ; Mizrahi et al, 2023b ). The aim is to utilize these features to predict whether an individual has a secure or insecure attachment style and to assess the specific contribution of each feature to this prediction.…”