A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding

Oct 10, 2023IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

A Neural Network Using Brain Waves to Decode Imagined Movements

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Abstract

The proposed multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) achieved 82.72% classification accuracy on the BCI competition IV 2a dataset.

TSFCNet utilizes a novel mechanism to extract spatial and temporal EEG features combined with frequency characteristics.
The network employs a MixConv-Residual block to obtain multiscale temporal features from filtered EEG data.
Three parallel convolution operations capture discriminative representations in spatial, frequency, and time-frequency domains.
Average pooling and variance layers effectively aggregate the extracted features.
Results indicate that TSFCNet outperforms state-of-the-art models in MI decoding with notable accuracy and kappa values.

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Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and lead to inaccurate decoding outcomes due to the redundant information. To address this limitation and make full use of the multi-domain EEG features, a multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) is proposed for MI decoding. The proposed network provides a novel mechanism that utilize the spatial and temporal EEG features combined with frequency and time-frequency characteristics. This network enables powerful feature extraction without complicated network structure. Specifically, the TSFCNet first employs the MixConv-Residual block to extract multiscale temporal features from multi-band filtered EEG data. Next, the temporal-spatial-frequency convolution block implements three shallow, parallel and independent convolutional operations in spatial, frequency and time-frequency domain, and captures high discriminative representations from these domains respectively. Finally, these features are effectively aggregated by average pooling layers and variance layers, and the network is trained with the joint supervision of the cross-entropy and the center loss. Our experimental results show that the TSFCNet outperforms the state-of-the-art models with superior classification accuracy and kappa values (82.72% and 0.7695 for dataset BCI competition IV 2a, 86.39% and 0.7324 for dataset BCI competition IV 2b). These competitive results demonstrate that the proposed network is promising for enhancing the decoding performance of MI BCIs.

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A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding

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