School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, China.
J Healthc Eng. 2021 Dec 22;2021:4073739. doi: 10.1155/2021/4073739. eCollection 2021.
Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by "enhancing the difficulty of imagination tasks," conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (-1.827) is less than that of LFMI (-1.3487) in the alpha band, so did (-3.4756 < -2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different (=0.0074 < 0.01; = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different (=0.0034 < 0.01; = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.
运动想象（MI）是指仅在没有实际运动的情况下进行心理运动的过程。MI 的研究在特征信息检测和机器学习解码算法方面取得了重大进展，但仍存在问题，例如整体识别率低和个体执行效果差异大，这使得 MI 的发展陷入瓶颈。针对这一瓶颈问题，本研究通过“增加想象任务的难度”来优化 MI 原始信号的质量，对 EEG 节律特征进行定性和定量分析，并使用 ERD 均值和识别率等定量指标，对不同任务（即高频运动想象（HFMI）和低频运动想象（LFMI））的下肢 MI 进行对比分析。研究结果验证了以下结论：在 alpha 频段，HFMI 的平均 ERD（-1.827）小于 LFMI 的平均 ERD（-1.3487），在 beta 频段也是如此（-3.4756 < -2.2891）。在 alpha 和 beta 特征频段，HFMI 的平均 ERD 小于 LFMI 的平均 ERD，两者的 ERD 值差异显著（=0.0074 < 0.01； = 0.945）。HFMI 的 ERD 强度 STD 值小于 LFMI 的 ERD 强度 STD 值，这表明在 HFMI 模式下，受试者的 ERD 强度个体差异较小。HFMI 的平均识别率高于 LFMI（87.84% > 76.46%），两种模式的识别率差异显著（=0.0034 < 0.01； = 0.429）。综上所述，本研究通过增加想象任务的难度来优化 MI 脑信号源的质量，达到提高参与者下肢 MI 整体识别率、降低个体执行效果和信号质量差异的目的。
