基于维度注意力与多尺度卷积网络的脑电分类方法研究

EEG Classification Based on Dimensional Attention and Multi-scale Convolutional Networks

摘要:

针对脑电信号的非平稳性、时变复杂和分类准确率较低的问题,以及传统机器学习方法在提取复杂特征方面的不足,提出了一种基于维度注意力机制的多尺度时空卷积神经网络分类模型(DIMS-CNN),旨在提高分类准确率,以更好地适用于实际应用场景。首先,对数据进行带通滤波和去伪迹,并进行了降采样和通道选择等预处理；随后,将经过处理的数据输入构建的时空卷积模型中,为了进一步增强网络的特征提取能力,加入了时序和通道的多维度注意力机制以及正则化技术；对于数据量不足的问题,采用了频带互换的方法进行数据增强,且提高了模型的泛化性能。分别在HGD数据集和实验室自采集数据集上进行验证,获得了90.97%和90.21%的平均分类准确率,发现所提方法在分类准确率上有显著提高。

Abstract:

This study proposes a spatio-temporal dynamic multiscale convolutional neural network (DMS-CNN) classification model based on a dimensional attention mechanism to improve classification accuracy and applicability to practical scenarios, in order to address the problems of non-stationarity, time-varying complexity, and low classification accuracy of electroencephalogram (EEG) signals, as well as the shortcomings of traditional machine learning methods in extracting complex features. First, the data are bandpass-filtered to eliminate artifacts and pre-processed using downsampling and channel selection. The processed data are then input into the constructed spatiotemporal convolution model, to further enhance the feature extraction capability of the network and multidimensional attention mechanisms of timing. This is followed by the incorporation of channel and regularization technology. To address the problem of insufficient data, a frequency-band exchange method is used to enhance the data, thereby improving the generalization performance of the model. Average classification accuracies of 90.97% and 90.21% were obtained for the HGD and self-collected laboratory datasets, respectively. Compared with other algorithms, the classification accuracy of this method was significantly improved.