Talukdar Upasana, Hazarika Shyamanta M, Gan John Q
Biomimetic and Cognitive Robotics Lab, Department of Computer Science and Engineering, Tezpur University, Tezpur, India.
Mechatronics and Robotics Lab, Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, India.
J Comput Neurosci. 2019 Feb;46(1):55-76. doi: 10.1007/s10827-018-0701-0. Epub 2018 Nov 29.
Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue. The growth of fatigue was monitored using the Kernel Partial Least Square (KPLS) algorithm on the remaining 6 participants which shows that MI induces substantial mental fatigue. Statistical analysis of the effect of fatigue on motor imagery performance shows that high fatigue level significantly decreases MI EEG separability. Collectively, these results portray an MI-fatigue inter-connection, emphasizing the necessity of developing adaptive MI BCI by tracking mental fatigue.
尽管长期以来人们一直认为心理状态会影响脑机接口（BCI）的性能，但支持这一假设的正式分析却很少。本研究使用脑电图（EEG）调查运动想象（MI）与精神疲劳之间的相互关系：a. 长时间的运动想象序列是否会导致精神疲劳；b. 精神疲劳是否会影响运动想象脑电图的类别可分离性。11名参与者参加了运动想象实验，其中5人因疲劳感过高而中途退出。使用核偏最小二乘法（KPLS）算法对其余6名参与者的疲劳增长情况进行监测，结果表明运动想象会引发显著的精神疲劳。对疲劳对运动想象表现的影响进行的统计分析表明，高疲劳水平会显著降低运动想象脑电图的可分离性。总体而言，这些结果描绘了运动想象与疲劳之间的联系，强调了通过跟踪精神疲劳来开发自适应运动想象脑机接口的必要性。
