• Bin Gu, Kun Wang, Long Chen, Jiatong He, Dingze Zhang, Minpeng Xu, Zhongpeng Wang, and Dong Ming.
• SUISHI (Tianjin) Intelligence Ltd, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China.
• Neuroscience. 2023 Oct 15; 530: 566556-65.

AbstractMotor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.Copyright © 2023 IBRO. Published by Elsevier Ltd. All rights reserved.

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