STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES Cognitive Neurodynamics Pub Date : 2024-07-21 DOI:10.1007/s11571-024-10154-5
Ming Meng, Bin Xu, Yuliang Ma, Yunyuan Gao, Zhizeng Luo
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Abstract

Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals. In this paper, we propose a spatio-temporal-graph attention network for channel selection (STGAT-CS) of EEG signals. We consider the EEG channels and their inter-channel connectivity as a graph and treat the channel selection problem as a node classification problem on the graph. We leverage the multi-head attention mechanism of graph attention network to dynamically capture topological relationships between nodes and update node features accordingly. Additionally, we introduce one-dimensional convolution to automatically extract temporal features from each channel in the original EEG signal, thereby obtaining more comprehensive spatiotemporal characteristics. In the classification tasks of the BCI Competition III Dataset IVa and BCI Competition IV Dataset I, STGAT-CS achieved average accuracies of 91.5% and 85.4% respectively, demonstrating the effectiveness of the proposed method.

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STGAT-CS:基于时空图注意网络的信道选择,用于基于 MI 的生物识别(BCI)技术
基于运动图像范例的脑机接口(BCI)通常利用多通道脑电图(EEG)来确保准确捕捉生理现象。然而,过多的信道往往包含冗余信息和噪声,这会大大降低 BCI 的性能。虽然已有大量关于脑电图通道选择的研究,但大多数研究都需要人工提取特征,而提取的特征很难完全代表脑电信号的有效信息。本文提出了一种用于脑电信号通道选择的时空图注意力网络(STGAT-CS)。我们将脑电图信道及其信道间连接视为一个图,并将信道选择问题视为图上的节点分类问题。我们利用图注意力网络的多头注意力机制来动态捕捉节点之间的拓扑关系,并相应地更新节点特征。此外,我们还引入了一维卷积,自动提取原始脑电信号中各通道的时间特征,从而获得更全面的时空特征。在BCI竞赛III数据集IVa和BCI竞赛IV数据集I的分类任务中,STGAT-CS的平均准确率分别达到91.5%和85.4%,证明了所提方法的有效性。
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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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