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http://hdl.handle.net/10397/105198
Title: | Dual attentive fusion for EEG-based brain-computer interfaces | Authors: | Du, Y Huang, J Huang, X Shi, K Zhou, N |
Issue Date: | 2022 | Source: | Frontiers in neuroscience, 2022, v. 16, 1044631 | Abstract: | The classification based on Electroencephalogram (EEG) is a challenging task in the brain-computer interface (BCI) field due to data with a low signal-to-noise ratio. Most current deep learning based studies in this challenge focus on designing a desired convolutional neural network (CNN) to learn and classify the raw EEG signals. However, only CNN itself may not capture the highly discriminative patterns of EEG due to a lack of exploration of attentive spatial and temporal dynamics. To improve information utilization, this study proposes a Dual Attentive Fusion Model (DAFM) for the EEG-based BCI. DAFM is employed to capture the spatial and temporal information by modeling the interdependencies between the features from the EEG signals. To our best knowledge, our method is the first to fuse spatial and temporal dimensions in an interactive attention module. This module improves the expression ability of the extracted features. Extensive experiments implemented on four publicly available datasets demonstrate that our method outperforms state-of-the-art methods. Meanwhile, this work also indicates the effectiveness of Dual Attentive Fusion Module. | Keywords: | Brain-computer interface Dual attentive fusion Electroencephalography Motor imagery P300 |
Publisher: | Frontiers Research Foundation | Journal: | Frontiers in neuroscience | ISSN: | 1662-453X | DOI: | 10.3389/fnins.2022.1044631 | Rights: | Copyright © 2022 Du, Huang, Huang, Shi and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. The following publication Du Y, Huang J, Huang X, Shi K and Zhou N (2022) Dual attentive fusion for EEG-based brain-computer interfaces. Front. Neurosci. 16:1044631 is available at https://doi.org/10.3389/fnins.2022.1044631. |
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