COMPLEXITY AND INFORMATION-BASED ANALYSIS OF THE ELECTROENCEPHALOGRAM (EEG) SIGNALS IN STANDING, WALKING, AND WALKING WITH A BRAIN–COMPUTER INTERFACE

Janarthanan Ramadoss, Norazryana Mat Dawi (), Karthikeyan Rajagopal and Hamidreza Namazi
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Janarthanan Ramadoss: Centre for Artificial Intelligence, Chennai Institute of Technology, Kundrathur, Chennai 600069, Tamil Nadu, India
Norazryana Mat Dawi: ��Independent Researcher, 525 West 8th Avenue, Vancouver, BC, Canada V5Z 1C6, Canada
Karthikeyan Rajagopal: ��Centre for Nonlinear Systems, Chennai Institute of Technology, Kundrathur, Chennai 600069, Tamil Nadu, India
Hamidreza Namazi: �Incubator of Kinanthropology Research, Faculty of Sports Studies, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic¶College of Engineering & Science, Victoria University, P. O. Box 14428, Melbourne, VIC 8001, Australia

FRACTALS (fractals), 2022, vol. 30, issue 01, 1-7

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.

Keywords: EEG Signals; Complexity; Information; Fractal Theory; Shannon Entropy (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22500414

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