Ahn Minkyu, Cho Hohyun, Ahn Sangtae, Jun Sung C
School of Computer Science and Electrical Engineering, Handong Global University, Pohang, South Korea.
Wadsworth Center, New York State Department of Health, Albany, NY, United States.
Front Hum Neurosci. 2018 Feb 15;12:59. doi: 10.3389/fnhum.2018.00059. eCollection 2018.
Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation ( = 0.64, < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: = 0.02 to = 0.54, < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.
性能变化是运动想象脑机接口（MI-BCI）中的一个关键问题，文献中已经报道了各种神经生理、心理和解剖学方面的相关因素。尽管此类研究的主要目的是为了对表现不佳者进行预筛选来预测MI-BCI性能，但缺乏关注用户对运动想象过程的感受并通过用户自我预测直接评估MI-BCI性能的研究。在本研究中，我们首先测试了每个用户关于运动想象实验数据集的自我预测想法。52名受试者参与了一项经典的两类运动想象实验，并被要求评估他们进行运动想象的难易程度以及预测自己的MI-BCI性能。在运动想象实验期间，记录了脑电图（EEG）；然而，没有向受试者提供关于运动想象的反馈。从EEG记录中，估计了离线分类准确率，并将其与受试者的几个问卷得分以及每个受试者对MI-BCI性能的自我预测进行了比较。受试者在运动想象任务期间的性能预测显示出高度正相关（ = 0.64， < 0.01）。有趣的是，观察到随着受试者在相关系数（任务前到第二次运行： = 0.02到 = 0.54， < 0.01）和均方根误差（任务前到第三次运行：17.7%到10%， < 0.01）中进行更多的运动想象任务，自我预测变得更加准确。我们证明了即使没有反馈信息，受试者也可以准确预测他们的MI-BCI性能。这意味着人类大脑是一个主动学习系统，通过自我体验内源性运动想象过程，它能够感知并采用该过程的质量。因此，相信用户能够预测MI-BCI性能，并且结果可能有助于更好地理解低性能并推动脑机接口的发展。
