ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Neuroscience Methods and Techniques
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1541062
Enhanced Visibility Graph for EEG Classification
Provisionally accepted
- 1Oslo Metropolitan University, Oslo, Norway
- 2University of South-Eastern Norway (USN), Kongsberg, Vestfold, Norway
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Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions. In this paper, we propose an end-to-end framework for EEG classification that integrates power spectral density (PSD) and visibility graph (VG) features together with deep learning (DL) techniques. Our framework offers a holistic approach for capturing both frequency-domain characteristics and temporal dynamics of EEG signals. We evaluate four DL architectures, namely multilayer perceptron (MLP), long short-term memory (LSTM) networks, InceptionTime and ChronoNet, applied to several datasets and in different experimental conditions. Results demonstrate the efficacy of our framework in accurately classifying EEG data, in particular when using VG features. We also shed new light on the relative strengths and limitations of different feature extraction methods and DL architectures in the context of EEG classification. Our work contributes to advancing EEG analysis and facilitating the development of more accurate and reliable EEG-based systems for neuroscience and beyond. The full code of this research work is available on https://github. com/asmab89/VisibilityGraphs.git.
Keywords: EEG classification, Visibility graph, feature learning, deep learning, Disease detection
Received: 06 Dec 2024; Accepted: 28 Apr 2025.
Copyright: © 2025 Belhadi, Lind, Djenouri and Yazidi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Asma Belhadi, Oslo Metropolitan University, Oslo, Norway
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