On-task theta power is correlated to motor imagery performance

This study aimed to evaluate on-task electroencephalographic spectral measures and its correlation to performance during a motor imagery (MI) task. By investigating this aspect, we hope to understand what makes some individuals MI "illliterates". Eighteen healthy subjects performed an experimental task whereby a cursor was moved to one of two targets (left and right) using only MI of the left and right hands. To evaluate the effect of aptitude, performance was measured as percentage of correct movement to target, and Mahalanobis distances were calculated between whole-scalp spectral patterns during left and right motor imagery. Then the correlation between performance and Mahalanobis distance was investigated for central, and whole-head topographies using Spearman's correlations. In central topographies, distances on alpha band were positively correlated with performance (ρ=0.562, p=0.032), while distances on theta band were negatively correlated to performance (ρ--0.648, p=0.018) in whole-head maps. The investigation of on-task whole-scalp differences allows a holistic comprehension of the neural basis of motor imagery, as well as how this leads to performance variations

Current brain activity is a predictor of longitudinal motor imagery performance

This study aimed to evaluate whether current electroencephalographic spectral measures can predict participant's performance during future sessions of a motor imagery task. By investigating this point, we hope to understand which spectral components are related to MI "literacy". Twelve healthy subjects performed a neurofeedback task whereby a cursor was moved to one of two targets (left and right) using only motor imagery of the corresponding hands. To evaluate the effect of aptitude, we measured the Mahalanobis' distances between whole-scalp spectral patterns in four frequency bands (theta, alpha, beta, and gamma) during the first session of left and right motor imagery. Later, we used these features as inputs in a Support Vector Regressor to predict performance during the following two sessions. The performance was measured as the percentage of trials where the cursor correctly reached the target. Since our sample was balanced, this approach predicted performance on sessions two and three with mean absolute errors of 15.07±12.94% and 11.98±11.40%, respectively. The most relevant feature in both cases was the Mahalanobis' distance in alpha. These results suggest that participants who can not evoke different patterns of alpha power during left- and right-hand motor imagery during the first session, also are less likely to improve during the following training sessions. The investigation of whole-scalp differences allows a holistic comprehension of the neural basis of motor imagery. This method also characterizes a potential predictor of performance for future applications of MI-based neurofeedback and brain-computer interfaces