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2023 | OriginalPaper | Buchkapitel

Deep Learning for Automatic Electroencephalographic Signals Classification

verfasst von : Nadia N. Sánchez-Pozo, Samuel Lascano-Rivera, Francisco J. Montalvo-Marquez, Dalia Y. Ortiz-Reinoso

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer Nature Switzerland

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Abstract

Automated electroencephalographic (EEG) signals classification using deep learning algorithms is an emerging technique in neuroscience that has the potential to detect brain pathologies such as epilepsy efficiently. In this process, deep learning algorithms are trained with labeled EEG signal datasets. However, due to the highly complex nature of EEG signals and the large amount of irrelevant information they contain, feature extraction techniques must be applied to reduce their dimensionality and focus on relevant information. This paper presents a comparative study on feature extraction methods for the classification of EEG recordings. The results demonstrate that the proposed classification algorithms and characterisation techniques are effective and suitable, as the accuracy metrics reach a value of 99.27%. The results presented in this paper contribute to the further development of automatic EEG signal classification methods based on deep learning.

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Titel: Deep Learning for Automatic Electroencephalographic Signals Classification
verfasst von: Nadia N. Sánchez-Pozo
Samuel Lascano-Rivera
Francisco J. Montalvo-Marquez
Dalia Y. Ortiz-Reinoso
Verlag: Springer Nature Switzerland
Buch: Bioinformatics and Biomedical Engineering
Print ISBN: 978-3-031-34952-2

Electronic ISBN: 978-3-031-34953-9

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-34953-9_20

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