Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning

Experienced meditators outperformed meditation-naïve subjects in one-dimensional and two-dimensional cursor control tasks using a sensorimotor rhythm-based brain–computer interface. Fewer meditators were classified as BCI inefficient. Meditators also showed a higher resting SMR predictor, more stable resting mu rhythm, and larger control signal contrast during the task, suggesting that meditation training may enhance BCI performance.

Study at a glance

Abstract

Sensorimotor rhythm (SMR)-based brain–computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery. Despite the non-invasiveness, ease of use, and low cost, this kind of BCI has limitations due to long training times and BCI inefficiency—that is, the SMR BCI control paradigm may not work well on a subpopulation of users. Meditation is a mental training method to improve mindfulness and awareness and is reported to have positive effects on one’s mental state. Here, we investigated the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in one-dimensional (1D) and two-dimensional (2D) cursor control tasks. We found numerical evidence that meditators outperformed control subjects in both tasks (1D and 2D), and there were fewer BCI inefficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups and showed that the meditators had a higher resting SMR predictor, more stable resting mu rhythm, and a larger control signal contrast than controls during the task.