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SantaMaría Vazquez, Eduardo Martínez Cagigal, Víctor Vaquerizo Villar, Fernando Hornero Sánchez, Roberto 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