A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding

Jul 27, 2023IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

A Neural Network Using Attention to Learn Timing Patterns for Decoding Imagined Movements from EEG

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Abstract

The proposed network achieves an average accuracy of 79.48%, improving performance by 2.30% on the BCIC-IV-2a dataset.

Deep learning methods can enhance motor imagery (MI)-based brain-computer interface (BCI) systems by decoding electroencephalography (EEG) signals.
Temporal dependencies among MI-related patterns in different stages of tasks may be critical for improving MI-EEG decoding performance.
A novel convolutional neural network (CNN) with an attention mechanism is designed to learn both spatial and spectral information from multi-view EEG data.
The network segments the output data into non-overlapping time windows to extract discriminative features related to MI.
A temporal attention module assigns varying weights to features from different time windows, potentially leading to more discriminative feature fusion.

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Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.

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A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding

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