CNN-based Approaches For Cross-Subject Classification in Motor Imagery:
From The State-of-The-Art to DynamicNet
- URL: http://arxiv.org/abs/2105.07917v1
- Date: Mon, 17 May 2021 14:57:13 GMT
- Title: CNN-based Approaches For Cross-Subject Classification in Motor Imagery:
From The State-of-The-Art to DynamicNet
- Authors: Alberto Zancanaro, Giulia Cisotto, Jo\~ao Ruivo Paulo, Gabriel Pires,
and Urbano J. Nunes
- Abstract summary: Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control.
accurately classifying MI from brain signals is essential to obtain reliable BCI systems.
Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques.
- Score: 0.2936007114555107
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motor imagery (MI)-based brain-computer interface (BCI) systems are being
increasingly employed to provide alternative means of communication and control
for people suffering from neuro-motor impairments, with a special effort to
bring these systems out of the controlled lab environments. Hence, accurately
classifying MI from brain signals, e.g., from electroencephalography (EEG), is
essential to obtain reliable BCI systems. However, MI classification is still a
challenging task, because the signals are characterized by poor SNR, high
intra-subject and cross-subject variability. Deep learning approaches have
started to emerge as valid alternatives to standard machine learning
techniques, e.g., filter bank common spatial pattern (FBCSP), to extract
subject-independent features and to increase the cross-subject classification
performance of MI BCI systems. 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 show-case the potentiality of
DynamicNet by implementing EEGNet, a well-established architecture for
effective EEG classification. Finally, we compare its performance with FBCSP in
a 4-class MI classification over public datasets. To explore its cross-subject
classification ability, we applied three different cross-validation schemes.
From our results, we demonstrate that DynamicNet-implemented EEGNet outperforms
FBCSP by about 25%, with a statistically significant difference when
cross-subject validation schemes are applied.
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