School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.
Sci Rep. 2024 Oct 9;14(1):23549. doi: 10.1038/s41598-024-73536-z.
In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.
在基于运动想象（MI）的脑机接口（BCI）领域，多通道脑电图（EEG）数据通常用于 MI 任务识别，以实现感觉补偿或精确的人机交互。然而，个体生理差异、环境变化或某些通道中的冗余信息和噪声会带来挑战，影响 BCI 系统的性能。在这项研究中，我们介绍了一种利用混合递归特征消除（H-RFE）结合残差图神经网络的通道选择方法，用于 MI 识别。这种通道选择方法采用递归特征消除策略，并集成了三种分类方法，即随机森林、梯度提升和逻辑回归，作为针对特定个体的自适应通道选择的评估器。为了充分利用多通道 EEG 的时空信息，本研究采用了嵌入残差块的图神经网络，实现了对运动想象的精确识别。我们在 SHU 数据集和 PhysioNet 数据集上进行了算法测试。实验结果表明，在 SHU 数据集上，利用总通道的 73.44%，跨会话 MI 识别准确率达到 90.03%。同样，在 PhysioNet 数据集上，使用 72.5%的通道数据，分类结果也达到 93.99%。与传统策略相比，如选择三个特定通道、基于相关的通道选择、基于互信息的通道选择以及基于 Pearson 系数和空间位置的自适应通道选择，该方法在 SHU 数据集上的分类准确率提高了 34.64%、10.8%、3.25%和 2.88%，在 PhysioNet 数据集上的分类准确率提高了 46.96%、5.04%、5.81%和 2.32%。
