EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning
In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called <italic>EEGSym</italic>. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variabi...
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| Main Authors: | , , , , |
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| Format: | Book |
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IEEE,
2022-01-01T00:00:00Z.
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| Summary: | In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called <italic>EEGSym</italic>. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variability and reducing BCI inefficiency, which has been estimated to affect 10-50% of the population. This convolutional neural network includes the use of inception modules, residual connections and a design that introduces the symmetry of the brain through the mid-sagittal plane into the network architecture. It is complemented with a data augmentation technique that improves the generalization of the model and with the use of transfer learning across different datasets. We compare <italic>EEGSym</italic>’s performance on inter-subject MI classification with ShallowConvNet, DeepConvNet, EEGNet and EEG-Inception. This comparison is performed on 5 publicly available datasets that include left or right hand motor imagery of 280 subjects. This population is the largest that has been evaluated in similar studies to date. <italic>EEGSym</italic> significantly outperforms the baseline models reaching accuracies of 88.6±9.0 on Physionet, 83.3±9.3 on OpenBMI, 85.1±9.5 on Kaya2018, 87.4±8.0 on Meng2019 and 90.2±6.5 on Stieger2021. At the same time, it allows 95.7% of the tested population (268 out of 280 users) to reach BCI control (≥70% accuracy). Furthermore, these results are achieved using only 16 electrodes of the more than 60 available on some datasets. Our implementation of <italic>EEGSym</italic>, which includes new advances for EEG processing with DL, outperforms previous state-of-the-art approaches on inter-subject MI classification. |
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| Item Description: | 1558-0210 10.1109/TNSRE.2022.3186442 |