Gu Yi, Hua Lei
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
Front Neurosci. 2021 May 6;15:685119. doi: 10.3389/fnins.2021.685119. eCollection 2021.
The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user's intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.
大脑是掌管人类活动的中枢神经系统。然而，在现代社会，越来越多的疾病威胁着大脑、神经和脊髓的健康，使得人类大脑无法与外界进行正常的信息交互。脑机接口的康复训练可以促进脑部疾病患者感觉运动皮层的神经修复。因此，用于运动成像的脑机接口研究对于脑部疾病患者恢复运动功能具有重要意义。由于脑电信号具有非平稳、非线性和个体差异等特点，现阶段脑电信号的分析与分类仍存在诸多困难。在本研究中，采用极限学习机（ELM）模型对运动成像脑电信号进行分类，识别用户意图并控制外部设备。考虑到单模态特征无法表征核心信息，本研究采用融合了时间和空间特征的融合特征作为最终特征数据。将融合特征输入到训练好的ELM分类器中，得到最终分类结果。利用脑机接口公开数据库中的两组脑机接口竞赛数据验证模型的有效性。实验结果表明，ELM模型在数据集IIb的分类任务中达到了0.7832的分类准确率，高于其他对比算法，且在不同受试者间表现出普遍适用性。此外，该模型在数据集IIIa分类任务中的平均识别率达到0.8347，与对比分类算法相比具有明显优势。分类效果虽小于同一项目冠军算法所得分类效果，但具有一定参考价值。
