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SantaMaría Vazquez, Eduardo 6a8836c814d2cc07 600 Martínez Cagigal, Víctor 13061702b3ec2dda 600 0000-0002-3822-1787 Vaquerizo Villar, Fernando 6d198fa6faee87eb 600 0000-0002-5898-2006 Hornero Sánchez, Roberto f6af2dd4a94089d7 600 0000-0001-9915-2570 2024-02-08T11:21:15Z 2024-02-08T11:21:15Z 2020 IEEE Transactions on Neural Systems and Rehabilitation Engineering, Diciembre, 2020, vol. 28 (12), pp. 2773 - 2782. https://uvadoc.uva.es/handle/10324/65991 10.1109/TNSRE.2020.3048106 2773 12 2782 IEEE Transactions on Neural Systems and Rehabilitation Engineering 28 Producción Científica In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications. DPI2017-84280-R, 0378_AD_EEGWA_2_P application/pdf eng IEEE info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional Brain-computer interfaces Event-related potentials P300 Deep learning Convolutional Neural Networks Inception Transfer learning EEG-inception: a novel deep convolutional Neural Network for assistive ERP-based brain-computer interfaces info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion https://ieeexplore.ieee.org/document/9311146 SI THUMBNAIL EEG-Inception_Accepted.pdf.jpg EEG-Inception_Accepted.pdf.jpg IM Thumbnail image/jpeg 2029 https://uvadoc.uva.es/bitstream/10324/65991/4/EEG-Inception_Accepted.pdf.jpg f71abff8bbec6b939a6286c0ef52a75f MD5 4 LICENSE license.txt license.txt text/plain 3929 https://uvadoc.uva.es/bitstream/10324/65991/3/license.txt 289d98c1665ee0d20312360f0e8643c8 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 817 https://uvadoc.uva.es/bitstream/10324/65991/2/license_rdf 975848b171c53b753f7e445c8b5bc0e1 MD5 2 ORIGINAL EEG-Inception_Accepted.pdf EEG-Inception_Accepted.pdf application/pdf 334064 https://uvadoc.uva.es/bitstream/10324/65991/1/EEG-Inception_Accepted.pdf a59bcd7ee0d1f6eaf58a04b659cb8877 MD5 1 10324/65991 oai:uvadoc.uva.es:10324/65991 2025-02-06 08:56:29.742 UVaDOC repositorio@uva.es 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