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bioRxiv · 10.1101/2022.04.26.487963

Neurofeedback training for improving motor performance in healthy adults: A systematic review and meta-analysis

Onagawa, R.·Muraoka, Y.·Hagura, N.·Takemi, M.
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Abstract

Neurofeedback training (NFT) refers to a training where the participants voluntarily aim to manipulate their own brain activity using the sensory feedback abstracted from their brain activity. NFT has attracted attention in the field of motor learning for its potential to become an alternative or additional training method for general physical training. In this study, a systematic review of NFT studies for motor performance improvements in healthy adults and a meta-analysis on the effectiveness of NFT were conducted. To identify relevant studies published between January 1st, 1990 to August 3rd, 2021, a computerized search was performed using the databases, Web of Science, Scopus, PubMed, JDreamIII, and Ichushi-Web. Thirty-two studies were identified for the qualitative synthesis and 13 randomized controlled trials (286 subjects) for the meta-analysis. The meta-analysis revealed significant effects of NFT for motor performance improvement examined at the timing after the last NFT session (standardized mean difference = 0.96, 95% CI = 0.40-1.53), but with the existence of publication biases and substantial heterogeneity among the trials. Subsequent subgroup meta-analysis demonstrated reliable benefits when the NFT is performed longer than 1 week. The effectiveness of NFT for each motor performance measurement (e.g., speed, accuracy, and hand dexterity) remains unclear because of high heterogeneity or due to small sample size. Further accumulation of empirical NFT studies for motor performance improvement will be necessary to provide reliable evidence about the NFT effects on specific motor skills and to safely incorporate NFT into real-world scenarios.

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Onagawa, R., Muraoka, Y., Hagura, N., Takemi, M.. 2022-04-27. Neurofeedback training for improving motor performance in healthy adults: A systematic review and meta-analysis. https://doi.org/10.1101/2022.04.26.487963

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