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Electroencephalographic Neurofeedback Training (EEG NFT) aims to improve sport performance by teaching athletes to control their mental states, leading to better cognitive, emotional, and physical outcomes. The psychomotor efficiency hypothesis suggests that optimizing brain function could enhance athletic ability, indicating the potential of EEG NFT. However, evidence for EEG-NFT's ability to alter critical brain activity patterns, such as sensorimotor rhythm and frontal midline theta-key for concentration and relaxation-is not fully established. Current research lacks standardized methods and comprehensive studies. This shortfall is due to inconsistent EEG target selection and insufficient focus on coherence in training. This review aims to provide empirical support for EEG target selection, conduct detailed control analyses, and examine the specificity of electrodes and frequencies to relation to the psychomotor efficiency hypothesis. Following the PRISMA method, 2,869 empirical studies were identified from PubMed, Science Direct, Web of Science, Embase, CNKI, and PsycINFO. Thirteen studies met the inclusion criteria: (i) proficient skill levels; (ii) use of EEG; (iii) neurofeedback training (NFT); (iv) motor performance metrics (reaction time, precision, dexterity, balance); (v) control group for NFT comparison; (vi) peer-reviewed English-language publication; and (vii) randomized controlled trial (RCT) design. Studies indicate that NFT can enhance sports performance, including improvements in shooting accuracy, golf putting, and overall motor skills, as supported by the psychomotor efficiency hypothesis. EEG NFT demonstrates potential in enhancing sports performance by optimizing performers' mental states and psychomotor efficiency. However, the current body of research is hampered by inconsistent methodologies and a lack of standardized EEG target selection. To strengthen the empirical evidence supporting EEG NFT, future studies need to focus on standardizing target selection, employing rigorous control analyses, and investigating underexplored EEG markers. These steps are vital to bolster the evidence for EEG NFT and enhance its effectiveness in boosting sport performance.
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http://dx.doi.org/10.3389/fpsyg.2024.1331997 | DOI Listing |
IEEE Trans Neural Syst Rehabil Eng
August 2025
Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency.
View Article and Find Full Text PDFBMC Med
May 2025
Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizmann St., Tel Aviv, 6423906, Israel.
Background: Fibromyalgia (FM), involving somatic, cognitive, and affective domains is often regarded as a hallmark central sensitization syndrome. Despite limited current therapeutic options, emerging understanding of its neural underpinnings offers the potential of applying novel neuromodulation strategies. Specifically, limbic dysregulation underlying abnormalities in pain modulation and somatic-affective processing, has been shown to play a key role in FM.
View Article and Find Full Text PDFBrain Connect
May 2025
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (PTSD). However, existing research lacks comprehensive methodologies and experimental designs. There are concerns in the field regarding the effectiveness and mechanistic interpretability of NF, prompting this study to conduct a systematic analysis of primary NF techniques and research outcomes in PTSD modulation.
View Article and Find Full Text PDFJ Theor Biol
July 2025
School of Physics, University of Sydney, Sydney, NSW 2006, Australia.
Objective: The alpha, mu, and tau rhythms all have frequencies of around 10 Hz in normal adult humans, with a range of 7-13 Hz. The beta rhythm, mu-associated activity, and tau-associated activity, are found at around twice those frequencies. The present objective is to use neural field theory (NFT) to explain the observed frequency structure and spatial topography, and to suggest a mechanism of reactivity, of all these rhythms in a unified way, and to predict other features not yet reported experimentally.
View Article and Find Full Text PDFJ Neuroeng Rehabil
April 2025
Department of Medical Science Industries, Chang Jung Christian University, Tainan, 711, Taiwan.
Objective: To investigate which brain activity frequency of electroencephalogram (EEG)-neurofeedback training (NFT) was the most effective for enhancing working memory (WM) and episodic memory (EM) in healthy participants through network meta-analysis (NMA).
Methods: Searched PubMed, Embase, and Cochrane Library for studies published from January 1990 to January 2025. We performed Bayesian NMA, pooling continuous outcome data using the standardized mean difference effect size (ES).