AUTHOR=Ahn Minkyu , Cho Hohyun , Ahn Sangtae , Jun Sung C. TITLE=User’s Self-Prediction of Performance in Motor Imagery Brain–Computer Interface JOURNAL=Frontiers in Human Neuroscience VOLUME=12 YEAR=2018 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2018.00059 DOI=10.3389/fnhum.2018.00059 ISSN=1662-5161 ABSTRACT=
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 (