EEG-DCNet：一种快速准确的运动想象脑电扩张卷积神经网络分类方法
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
AI总结：
针对脑机接口中运动想象脑电分类的关键挑战，提出EEG-DCNet模型，通过多分支扩张卷积、滑动窗口和注意力机制提升准确性与效率，在三个数据集上优于SOTA且参数更少。
摘要：
基于脑电图（EEG）的运动想象（MI）分类是脑机接口（BCI）技术中一项关键且具挑战性的任务，对帮助功能障碍患者恢复行动能力意义重大。我们提出一种新型多尺度扩张卷积神经网络（CNN）模型——EEG扩张卷积网络（DCNet），以提升基于EEG的MI分类任务的准确性和效率。在EEG-DCNet中，我们融入1×1卷积层，并采用多分支并行扩张卷积架构，以捕捉EEG信号的高度非线性特征和多尺度特征。此外，我们利用滑动窗口增强时间一致性，借助注意力机制提高识别用户意图的准确性。通过BCI-IV-2a、BCI-IV-2b和High-Gamma数据集的实验结果表明，EEG-DCNet在分类准确率和Kappa系数方面优于现有最先进（SOTA）方法。此外，由于EEG-DCNet所需参数数量更少，训练效率和内存消耗也得到了改善。实验代码已开源，地址为https://github.com/Kanyooo/EEG-DCNet。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
