FRACTAL THEORY IN FUNCTIONAL BRAIN MAPPING: FROM SENSOR SPACE TO SOURCE RECONSTRUCTION

Najmeh Pakniyat, Gaurav Agarwal, Penhaker Marek, Ondrej Krejcar and Hamidreza Namazi
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Najmeh Pakniyat: 30 Shore Breeze Drive, Toronto, ON M8V 0J1, Canada
Gaurav Agarwal: School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
Penhaker Marek: Faculty of Electrical Engineering and Computer Science (FEEC), VSB–Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czech Republic
Ondrej Krejcar: Faculty of Electrical Engineering and Computer Science (FEEC), VSB–Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czech Republic
Hamidreza Namazi: Faculty of Electrical Engineering and Computer Science (FEEC), VSB–Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czech Republic4Biomedical Signal and Image Processing Lab, Galgotias University, Greater Noida, Uttar Pradesh, India5School of Engineering, Monash University, Selangor, Malaysia

FRACTALS (fractals), 2025, vol. 33, issue 09, 1-12

Abstract: Fractal theory provides a powerful mathematical approach for quantifying nonlinear, self-similar, and scale-invariant patterns inherent in brain activity. This review surveys the application of fractal and multifractal analyses across varying spatial resolutions — from raw EEG/MEG signals in sensor space to cortical activity derived from source reconstruction techniques. Particular attention is given to how these fractal measures are used to study cognitive functions such as attention, memory, and sensory processing. We explore the utility of key fractal metrics such as the fractal dimension, Hurst exponent, and multifractal spectrum in capturing the dynamics of neural processes. A key novelty of this review lies in highlighting how fractal features can be integrated with source localization algorithms, enabling spatially resolved complexity mapping of the cortex. This dual-level framework — linking temporal complexity with anatomical localization — offers a richer and more precise view of brain function than traditional approaches. The findings underscore the potential of fractal analysis to enrich our understanding of brain dynamics and to drive innovation in neurotechnology.

Keywords: Fractal Theory; Source Localization; Brain Mapping; Multifractal Analysis; Sensor Space; BCI (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25300132

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