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

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.

Studies to Overcome Brain–Computer Interface Challenges

“…Over the past decades, there have been lots of BCI studies. They were usually focused on the methods to improve the prediction accuracy [5,8,23,[30][31][32], raise the number of commands [12,33], increase the information transfer rate (ITR) [34][35][36][37][38], or reduce the training efforts [7,30,34,39]. To enhance the prediction accuracy, new classification algorithms [30,40,41] or feature extraction methods have been proposed [31,32,42].…”

“…The motor cortex's alpha wave (8~13 Hz) and beta wave (13~30 Hz) will increase or decrease according to the movement intention. For example, when users want to move their hands or feet, the power of alpha and beta waves decreases on the corresponding motor cortex [2,[5][6][7][8]. Therefore, the BCI system can predict a left hand, a right hand, or feet movement intention using power change of the brain area.…”

“…SMR-BCIs are intuitive. However, this method requires a long training time for movement imagination [7]. P300 is a positive peak of the brain signal on the parietal region about 300 ms after the stimulation [5].…”

Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain–computer interface training of a cybathlete who has tetraplegia

“…Mindfulness is a metacognitive process as it requires self-regulation of attention to control cognitive processes while simultaneously monitoring conscious experience [ 44 ]. Individuals who are experienced in meditation skills have been found to demonstrate higher resting SMR power, a more stable resting mu rhythm, and greater BCI control, compared to those who have not practiced meditation techniques [ 45 ]. Furthermore, mindfulness-based stress reduction (MBSR) training has been found to improve BCI learning and performance on BCI tasks that communicate the user’s intent via motor imagery commands and volitional rest [ 46 ].…”

Competing at the Cybathlon Championship for Athletes With Disabilities: Long-Term Motor Imagery Brain-Computer Interface Training of a Tetraplegic Cybathlete

“…Mindfulness is a metacognitive process as it requires self-regulation of attention to control cognitive processes -while simultaneously monitoring conscious experience [40]. Individuals who are experienced in meditation skills have been found to demonstrate higher resting SMR power, a more stable resting mu rhythm, and greater BCI control, compared to those who are not practiced in meditation techniques [41]. Furthermore, mindfulness-based stress reduction (MBSR) training has been found to improve BCI learning and performance on BCI tasks that communicate the user's intent via motor imagery commands and volitional rest [42].…”