School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Department of Neurorehabilitation and Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin 300350, China.
Neural Plast. 2020 Dec 7;2020:8863223. doi: 10.1155/2020/8863223. eCollection 2020.
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.
运动想象（MI）是脑机接口（BCI）研究的重要组成部分，它可以解码受试者的意图并帮助重塑中风患者的神经系统。因此，基于脑电图（EEG）的运动想象的准确解码受到了广泛关注，尤其是在康复训练的研究中。我们提出了一种新颖的基于多频脑网络的深度学习框架，用于运动想象解码。首先，从多通道 MI 相关 EEG 信号构建多频脑网络，每个层对应于特定的脑频带。多频脑网络的结构与大脑活动模式相匹配，结合了通道和多频的信息。滤波器组共空间模式（FBCSP）算法在空间域中对基于 MI 的 EEG 信号进行滤波，以提取特征。进一步，设计了一个多层卷积网络模型，以准确区分不同的 MI 任务，从而允许提取和利用多频脑网络中的拓扑结构。我们使用公共 BCI 竞赛 IV 数据集 2a 和公共 BCI 竞赛 III 数据集 IIIa 来评估我们的框架，并在第一个数据集上获得了最先进的结果，即 BCI 竞赛 IV 数据集 2a 的平均准确率为 83.83%，kappa 值为 0.784，BCI 竞赛 III 数据集 IIIa 的准确率为 89.45%，kappa 值为 0.859。所有这些结果表明，我们的框架可以有效地从多通道 EEG 信号中分类不同的 MI 任务，并在中风患者的神经系统重塑研究中显示出巨大的潜力。
