| 引用本文: | 李卫校a ,凌六一a,b.基于 EEG-TCNet 的运动想象脑电识别方法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（1）：123-128 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 针对以深度学习为解码的方法在运动想象脑电信号识别过程中仅对原始的运动想象脑电信号进行特
征提取而不进行样本扩充和往往采用单一尺度的卷积对多频段的运动想象脑电信号进行特征提取,无法充分发掘
各频段之间相关性的问题,在主流 EEG-TCNet 解码方法的基础上提出了一种样本扩充和多尺度的解码方法。 方法
首先,对运动想象脑电信号进行分割,以增加数据集样本数,将运动想象脑电信号等间隔下采样成 3 个不同的子序
列,每个子序列都含有与原始运动想象脑电信号相同的数据特征;其次,使用 EEGNet 对每个子序列进行特征提取,
对不同的子序列使用不同尺度的 EEGNet 以便提取不同频段的特征;之后,对每个经过 EEGNet 提取后的子序列采
用一种基于卷积滑动的方法再进分割,充分挖掘每个子序列潜在的信息;再次,将每个处理后的子序列传入到时间
卷积网络进行特征提取和降维;最后,对所有处理后的子序列进行拼接、平均操作,并传入到全连接层进行识别。
结果 在公开的 BCI 竞赛数据集 IV-2a 上进行验证,所做出改进的网络相对于 EEG-TCNet、EEGNet 的解码准确度
分别有 5. 19%和 7. 7%的提升。 结论 证明所做出改进的网络在运动想象脑电信号识别任务中具有更理想的解码
性能。 |
| 关键词: EEG-TCNet 运动想象脑电信号 卷积神经网络 时间卷积网络 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Motor Imagery EEG Recognition Method Based on EEG-TCNet |
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LI Weixiaoa LING Liuyia b
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a. School of Electrical and Information Engineering b. School of Artificial Intelligence Anhui University of Science and
Technology Anhui Huainan 232001 China
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| Abstract: |
| Objective This study addresses the limitations of deep learning-based methods in recognizing motor imagery
electroencephalography EEG signals which primarily focus on feature extraction from raw signals without sample
augmentation and often utilize single-scale convolutions to extract features from multi-band EEG signals. This approach
fails to fully explore the correlations between different frequency bands. Therefore a sample augmentation and multi-scale
decoding method was proposed based on the mainstream EEG-TCNet decoding technique. Methods First the motor
imagery EEG signals were segmented to increase the number of samples in the dataset. The motor imagery EEG signals
were downsampled at equal intervals into three different subsequences with each subsequence containing the same data
characteristics as the original motor imagery EEG signal. Next EEGNet was used to extract features from each
subsequence employing different scales of EEGNet for different subsequences to capture features from various frequency
bands. Afterward a convolutional sliding method was applied to further segment each subsequence processed by EEGNet
fully exploring the latent information of each subsequence. Subsequently each processed subsequence was fed into a
temporal convolutional network for feature extraction and dimensionality reduction. Finally all processed subsequences were concatenated and averaged which were then input into a fully connected layer for recognition. Results The proposed
improved network was validated on the public BCI Competition Dataset IV-2a showing an increase in decoding accuracy
of 5. 19% and 7. 7% compared with EEG-TCNet and EEGNet respectively. Conclusion The improved network
demonstrates better decoding performance in motor imagery EEG signal recognition tasks. |
| Key words: EEG-TCNet motion imagery EEG signals convolutional neural network temporal convolution network |
