Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as “BCI-inefficiency,” in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection ( r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time.

Functional electrical stimulation (FES) is a common rehabilitation method for the purpose of recovery of paralyzed muscle by means of sequential electrical stimulation. Reports indicate that active participation by the patient, as opposed to simple stimulation, leads to improved recovery when using FES and other rehabilitation techniques. In this paper, we investigate the neurophysiological effect of an active participant's intention in the FES rehabilitation task. To observe the difference in brain signal between intentional and involuntary movement during FES, electroencephalography and near-infrared spectroscopy were simultaneously measured in the motor cortex area. The result showed that the presence of intention affects the activation of the brain significantly in both hemodynamic responses (near-infrared spectroscopy) and electrical (electroencephalography) patterns, and the accuracy of classification between passive and active mental states during FES was 85.3%. Our result implies the possibility to quantify motivation, or active participation, during rehabilitation, which has not been considered a measurable value in the rehabilitation field.