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September 24, 2024
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November 28, 2024
Published
June 1, 2025
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Abstract

Electroencephalogram (EEG) signals provide a window through which we can view brain activity, are of great importance for neurological diagnosis, developing Brain-Computer Interface (BCI), and cognitive neuroscience. Despite the importance of EEG data, they being complex and non-stationary make it difficult for analysis. With the development of machine learning (ML) and deep learning (DL) as state-of-the-art methods for decoding EEG signals, attempting to provide optimal performance in both accuracy and computational time costs when it comes to the problem of extreme complexity such as classification of huge dataset sizes. This paper presents a comprehensive review of the most recent ML and DL techniques in EEG signal analysis. We examine the latest methods — Convolutional Neural Networks (CNNs), Transformer models, Recurrent Neural Networks (RNNs), and both hybrid and traditional architectures with their individual undertakings in tasks of seizure detection, emotion classification, and motor imagery to evaluate each approach efficiency. Overall, the results validate the high transformative efficacy of ML and DL in the EEG signal domain which might provide a key towards optimizing our current knowledge of brain function, as well as serving to increase diagnostic accuracy in clinical environments.

Keywords

Electroencephalogram, Machine learning, Convolutional Neural Networks, Transformer-Based Models, Recurrent Neural Networks, Hybrid Architectures

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Copyright (c) 2025 The Author(s), under exclusive license to the University of Thi-Qar

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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