用空洞卷积快速准确分类脑电运动想象信号
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
- 多分支空洞卷积捕捉脑电信号多尺度非线性特征
- 在三个数据集上准确率和一致性指标均优于现有方法
- 参数少、训练快，适合实时脑机接口应用
基于脑电图（EEG）的运动想象（MI）分类是脑机接口（BCI）技术中的关键挑战任务，对帮助功能障碍患者恢复行动能力具有重要意义。本文提出一种新型多尺度空洞卷积神经网络（EEG-DCNet），以提升EEG-MI分类的准确率与效率。通过引入1×1卷积层和多分支并行空洞卷积结构，有效捕捉脑电信号的高阶非线性特性和多尺度特征。同时采用滑动窗口增强时间一致性，结合注意力机制提高用户意图识别精度。在BCI-IV-2a、BCI-IV-2b和High-Gamma数据集上的实验表明，EEG-DCNet在分类准确率和Kappa评分上均超越现有最先进方法。此外，由于参数量更少，训练效率与内存消耗也显著改善。实验代码已开源。
原文摘要 · Abstract (English)
The electroencephalography (EEG)-based motor imagery (MI) classification is a critical and challenging task in brain-computer interface (BCI) technology, which plays a significant role in assisting patients with functional impairments to regain mobility. We present a novel multi-scale atrous convolutional neural network (CNN) model called EEG-dilated convolution network (DCNet) to enhance the accuracy and efficiency of the EEG-based MI classification tasks. We incorporate the $1\times1$ convolutional layer and utilize the multi-branch parallel atrous convolutional architecture in EEG-DCNet to capture the highly nonlinear characteristics and multi-scale features of the EEG signals. Moreover, we utilize the sliding window to enhance the temporal consistency and utilize the attension mechanism to improve the accuracy of recognizing user intentions. The experimental results (via the BCI-IV-2a ,BCI-IV-2b and the High-Gamma datasets) show that EEG-DCNet outperforms existing state-of-the-art (SOTA) approaches in terms of classification accuracy and Kappa scores. Furthermore, since EEG-DCNet requires less number of parameters, the training efficiency and memory consumption are also improved. The experiment code is open-sourced at \href{https://github.com/Kanyooo/EEG-DCNet}{here}.
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