The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a fundamental, as well as challenging, task to provide reliable control of robotic devices to support people suffering from neuro-motor impairments, e.g., in brain-computer interface (BCI) applications. Recently, deep learning approaches have been able to extract subject-independent features from EEG, to cope with its poor SNR and high intra-subject and cross-subject variability. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We showcase the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with the filter bank common spatial pattern (FBCSP) in a 4-class MI task (data from a public dataset). To infer cross-subject classification performance, we applied three different cross-validation schemes. From our results, we show that EEGNet implemented with DynamicNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multi-class MI classification scenarios. In the future, it is expected to improve DynamicNet to implement new architectures to further investigate cross-subject classification of MI tasks in real-world scenarios.

Zancanaro, A., Cisotto, G., Paulo, J., Pires, G., & Nunes, U. (2021). CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the state-of-the-art to dynamicnet. In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021 (pp.1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/CIBCB49929.2021.9562821].

CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the state-of-the-art to dynamicnet

Cisotto G.;
2021

Abstract

The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a fundamental, as well as challenging, task to provide reliable control of robotic devices to support people suffering from neuro-motor impairments, e.g., in brain-computer interface (BCI) applications. Recently, deep learning approaches have been able to extract subject-independent features from EEG, to cope with its poor SNR and high intra-subject and cross-subject variability. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We showcase the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with the filter bank common spatial pattern (FBCSP) in a 4-class MI task (data from a public dataset). To infer cross-subject classification performance, we applied three different cross-validation schemes. From our results, we show that EEGNet implemented with DynamicNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multi-class MI classification scenarios. In the future, it is expected to improve DynamicNet to implement new architectures to further investigate cross-subject classification of MI tasks in real-world scenarios.
Si
paper
Scientifica
Deep learning, EEG, Tools, Brain modeling, Feature extraction, Electroencephalography, Reliability
English
2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021
978-1-6654-0112-8
Zancanaro, A., Cisotto, G., Paulo, J., Pires, G., & Nunes, U. (2021). CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the state-of-the-art to dynamicnet. In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021 (pp.1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/CIBCB49929.2021.9562821].
Zancanaro, A; Cisotto, G; Paulo, J; Pires, G; Nunes, U
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/370035
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