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 (r = 0.64, p < 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: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 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.
Highlights
Motor imagery based brain–computer interface (BCI) has become an increasingly prevalent process, and in recent decades researchers have made valuable achievements and demonstrated the feasibility of BCI for various applications, such as communication, control, rehabilitation, entertainment, and others (Wolpaw et al, 2002; Millán et al, 2010; Ortner et al, 2012; LaFleur et al, 2013; Ahn M. et al, 2014; Bundy et al, 2017; Guger et al, 2017)
According to a recent review (Ahn and Jun, 2015), researchers have approached this problem from different perspectives, all aiming to answer one question: “What is the best correlate of performance variation?” The aims of such studies can be summarized as follows: First, identify what distinct characteristics exist in poor performers; second, understand why these traits are common in the lower performers; and, lastly, use the correlates to classify the poor performers in advance, prior to using BCI
To address the issue of whether a user’s self-predicted score is correlated with offline BCI performance and, if so, how long it may take for the user to get a sense of the connection, we investigated the user’s self-assessed parameters, including mental and physical states, the quality of motor imagery, and self-predictions of motor imagery performance before and between runs
Summary
Motor imagery based brain–computer interface (BCI) has become an increasingly prevalent process, and in recent decades researchers have made valuable achievements and demonstrated the feasibility of BCI for various applications, such as communication, control, rehabilitation, entertainment, and others (Wolpaw et al, 2002; Millán et al, 2010; Ortner et al, 2012; LaFleur et al, 2013; Ahn M. et al, 2014; Bundy et al, 2017; Guger et al, 2017). These methods include introducing advanced signal processing techniques such as co-adaptive learning (Vidaurre et al, 2011; Xia et al, 2012; Merel et al, 2015), training users until they are able to generate classifiable signals (Mahmoudi and Erfanian, 2006; Hwang et al, 2009; Tan et al, 2014), and brain tuning that shifts the current brain state to a better state for motor imagery using tactile (Ahn S. et al, 2014) or electrical (Pichiorri et al, 2011; Wei et al, 2013; Yi et al, 2017) stimulation. In the studies of performance variation, such functionality of the brain is not seriously considered
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