BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition

Abstract Objective. Brain–computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one’s neural activity, measured by electroencephalography (EEG) signals. In recent years, convolutional neural networks (CNNs) have been widely used to perform automatic fea...

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Bibliographic Details
Published in:Journal of neural engineering 2023-10, Vol.20 (5), p.56014
Main Authors: Barmpas, Konstantinos, Panagakis, Yannis, Adamos, Dimitrios A, Laskaris, Nikolaos, Zafeiriou, Stefanos
Format: Article
Language:English
Subjects:
brain-computer interface (BCI)
differentiable signal processing
electroencephalography (EEG)
learnable filters
motor-imagery (MI)
wavelet scattering
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Summary:Abstract Objective. Brain–computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one’s neural activity, measured by electroencephalography (EEG) signals. In recent years, convolutional neural networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available. Approach. In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations. Main results. We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier. Significance. In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.
ISSN:1741-2560
1741-2552
DOI:10.1088/1741-2552/acf78a