SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals.

Autor: Borra D; Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi' (DEI), University of Bologna, Cesena, Forlì-Cesena, Italy. Electronic address: davide.borra2@unibo.it., Paissan F; Fondazione Bruno Kessler, Povo, Trento, Italy., Ravanelli M; Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada; Mila - Quebec AI Institute, Montreal, Quebec, Canada.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Nov; Vol. 182, pp. 109097. Date of Electronic Publication: 2024 Sep 11.
DOI: 10.1016/j.compbiomed.2024.109097
Abstrakt: Deep learning has revolutionized EEG decoding, showcasing its ability to outperform traditional machine learning models. However, unlike other fields, EEG decoding lacks comprehensive open-source libraries dedicated to neural networks. Existing tools (MOABB and braindecode) prevent the creation of robust and complete decoding pipelines, as they lack support for hyperparameter search across the entire pipeline, and are sensitive to fluctuations in results due to network random initialization. Furthermore, the absence of a standardized experimental protocol exacerbates the reproducibility crisis in the field. To address these limitations, we introduce SpeechBrain-MOABB, a novel open-source toolkit carefully designed to facilitate the development of a comprehensive EEG decoding pipeline based on deep learning. SpeechBrain-MOABB incorporates a complete experimental protocol that standardizes critical phases, such as hyperparameter search and model evaluation. It natively supports multi-step hyperparameter search for finding the optimal hyperparameters in a high-dimensional space defined by the entire pipeline, and multi-seed training and evaluation for obtaining performance estimates robust to the variability caused by random initialization. SpeechBrain-MOABB outperforms other libraries, including MOABB and braindecode, with accuracy improvements of 14.9% and 25.2% (on average), respectively. By enabling easy-to-use and easy-to-share decoding pipelines, our toolkit can be exploited by neuroscientists for decoding EEG with neural networks in a replicable and trustworthy way.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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