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。
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 diferent scales of EECNet 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 EEC-TCNet and EEGNet,respectively.Conclusion The improved network demonstrates better decoding performance in motor imagery EEG signal recognition tasks.
作者
李卫校
凌六一
LI Weixiao;LING Liuyi(School of Electrical and Information Engineering,Anhui University of Science and Technology,Anhui Huainan 232001,China;School of Artificial Intelligence,Anhui University of Science and Technology,Anhui Huainan 232001,China)
出处
《重庆工商大学学报(自然科学版)》
2025年第1期123-128,共6页
Journal of Chongqing Technology and Business University:Natural Science Edition
