# Mind-body awareness training and brain-computer interface

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2020 · $394,665

## Abstract

Summary
Brain-computer interface (BCI) technology is currently the subject of intense research interest, as it holds the
promise to assist numerous patients suffering from neuromuscular disorders and other systemic and brain
diseases. BCI systems allow users to interact with their environment without muscular control, by sensing and
decoding the user's thoughts. Noninvasive BCI uses electroencephalography (EEG) to sense user's cognitive
states; in EEG, scalp electrodes sense small potential changes arising from underlying neural activity. The
effectiveness of BCI, however, depends critically upon training the user's cognitive abilities: users must learn to
produce reliable patterns of brain activity that the BCI can decode from its sensors. Some users are able to
complete this training with relative ease, but for others the learning process is difficult and lengthy. Thus,
methods that can improve training promise to greatly enhance the usefulness of BCI in most applications,
benefiting the many patients who may be aided with BCI technology. Mind-body awareness training (MBAT)
has shown large effects upon both cognitive abilities and brain activity. MBAT emphasizes meditation practice
that focuses on body states, and it has been shown to enhance factors that are most critical for sensorimotor-
rhythm-based BCI: sustained attention, motor imagery, and generation of rhythmic neural signals. The general
goal of the proposed research is to investigate whether and how experience with MBAT can improve subjects'
ability to learn and use a sensorimotor-rhythm-based BCI. The specific aims of the proposed research are as
follows: Aim 1: We will test whether MBAT training has significant impact on learning of BCI skills. We will
study human subjects with various levels of MBAT experience and compare them with controls. Aim 2: We will
use and further develop novel multimodal neuroimaging methods, along with extensive behavioral testing, to
identify the neurocognitive components of MBAT that aid learning of BCI skills. Comprehensive analyses will
combine imaging and neurocognitive results to identify brain regions responsible for the factors that produce
improvement in BCI. The successful completion of the proposed research may allow MBAT training to become
a best practice in BCI use, increasing BCI signal quality and reducing its training time. It will also better the
understanding on how mind-body intervention works through innovative neuroimaging approaches.
Understanding the neurocognitive basis of improvement may allow the production of enhanced training
regimens, both with and without MBAT, including MBAT-like training specifically tailored to optimize BCI.

## Key facts

- **NIH application ID:** 9937649
- **Project number:** 5R01AT009263-06
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** BIN HE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $394,665
- **Award type:** 5
- **Project period:** 2018-02-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9937649

## Citation

> US National Institutes of Health, RePORTER application 9937649, Mind-body awareness training and brain-computer interface (5R01AT009263-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9937649. Licensed CC0.

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