Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band
Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals’ mindfulness practice as well as performance on a sustained attention task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.
Code (0)
등록된 구현이 없습니다.
Tasks
Brain Computer InterfaceEEGElectroencephalogram (EEG)Similar Papers 제목 키워드 기반
Easing Seasickness through Attention Redirection with a Mindfulness-Based Brain--Computer Interface
Seasickness is a prevalent issue that adversely impacts both passenger experiences and the operational efficiency of maritime crews. While techniques that redirect attention have proven effective in alleviating motion si…
Brain Computer InterfaceEEGInteroception Underlies The Therapeutic Effects of Mindfulness Meditation for Post-Traumatic Stress Disorder: A Randomized Clinical Trial
Mindfulness-based interventions have proven its efficacy in treating post-traumatic stress disorder (PTSD), but the underlying neurobiological mechanism is unknown. To determine the neurobiological mechanism of action of…
EEGElectroencephalogram (EEG)Subject-Independent Classification of Brain Signals using Skip Connections
Untapped potential for new forms of human-to-human communication can be found in the active research field of studies on the decoding of brain signals of human speech. A brain-computer interface system can be implemented…
Brain Computer InterfaceClassificationDecoderA Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BC…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery+1Automatic Control of Reactive Brain Computer Interfaces
This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback al…
Brain Computer InterfaceTransfer Learning