Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
Comput Methods Biomech Biomed Engin. 2024 Aug;27(11):1430-1447. doi: 10.1080/10255842.2023.2244110. Epub 2023 Aug 7.
Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this problem will greatly affect real-world applications. Most of the proposed methods are based on raw signal processing techniques. Known as prior knowledge, the structural-functional information and interregional connections can improve signal processing accuracy. It is possible to correctly perceive the generated signals by considering the brain structure (i.e. anatomical units), the source of signals, and the structural-functional dependence of different brain regions (i.e. effective connection) that are the semantic generators of signals. This study employed electroencephalograph (EEG) signals based on the activity of brain regions (cortex) and effective connections between brain regions based on dynamic causal modeling to solve the MI problem. EEG signals, as well as effective connections between brain regions to improve the interpretability of MI action, were fed into the architecture of Graph Convolutional Neural Network (GCN). The proposed model allowed GCN to extract more discriminative features. The results indicated that the proposed method was successful in developing a model with a MI detection accuracy of 93.73%.
分类为生物医学信号处理，脑信号处理在人机交互（HCI）和医学诊断中起着关键作用。运动想象（MI）问题是该领域的一个重要研究领域。这个问题的准确解决方案将极大地影响现实世界的应用。大多数提出的方法都是基于原始信号处理技术。作为先验知识的结构-功能信息和区域间连接可以提高信号处理的准确性。通过考虑大脑结构（即解剖单位）、信号源以及不同大脑区域的结构-功能依赖性（即有效连接），这些是信号的语义生成器，就有可能正确感知生成的信号。本研究采用基于脑区（皮层）活动的脑电图（EEG）信号和基于动态因果建模的脑区之间的有效连接来解决 MI 问题。EEG 信号以及脑区之间的有效连接可以提高 MI 动作的可解释性，这些信号被输入到图卷积神经网络（GCN）的架构中。所提出的模型允许 GCN 提取更具判别力的特征。结果表明，所提出的方法成功地开发了一种 MI 检测准确率为 93.73%的模型。
