ArticleCerebral cortex (New York, N.Y. : 1991)2021
Mindfulness Improves Brain-Computer Interface Performance by Increasing Control Over Neural Activity in the Alpha Band.
Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 60 citations in OpenAlex.
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Brain-computer interface control with artificial intelligence copilots.Nature machine intelligence · 2025Article
- EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.Nature communications · 2025Article
- Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine.Integrative medicine research · 2025Article
- Limited Validity of Breath-Counting as a Measure of Mindfulness in Ruminative Adolescents.Psychophysiology · 2025Article
- Continuous Reaching and Grasping With a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Motor imagery-based brain-computer interfaces: an exploration of multiclass motor imagery-based control for Emotiv EPOC X.Frontiers in neuroinformatics · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.Scientific data · 2024Article
- EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
- Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface.Brain sciences · 2024Article
- Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.PNAS nexus · 2024Article
- Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control.IEEE transactions on bio-medical engineering · 2024Article
- Tailoring brain-machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Continuous shared control of a mobile robot with brain-computer interface and autonomous navigation for daily assistance.Computational and structural biotechnology journal · 2023Article
- Effects of brain-Computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial.Frontiers in psychology · 2023Article
- Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.Journal of neuroengineering and rehabilitation · 2022Article
- On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2022Article
- Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface.Frontiers in human neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
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 electroencephalogram 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 breath counting 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.