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.

Neurofeedback training (NFT) has emerged as a promising technique for enhancing sports performance by enabling individuals to self‐regulate their neural activity. However, only 53% of the 13 included studies, all of which published before 2021, in the latest meta‐analyses of NFT and motor performance focused on motor performance outcomes. Due to the rapid development of neurofeedback, 8 high‐quality articles were published in 2023–2024 alone. Therefore, there is a need for a new meta‐analysis to update the impact of NFT on sports performance. In this systematic review and meta‐analysis, we have not only reassessed the knowledge of the effect of EEG neurofeedback in motor performance but have also incorporated a standardized methodology, known as the CRED‐nf checklist (Consensus on the reporting and experimental design of clinical and cognitive‐behavioral neurofeedback studies), for methodological evaluation of previous EEG neurofeedback studies. The study protocol was pre‐registered, and a systematic search was conducted across major databases to identify relevant randomized controlled trials. A total of 25 studies were included in the qualitative synthesis, with 21 studies eligible for the meta‐analysis. The meta‐analysis revealed a moderate positive effect of NFT on sport motor tasks, with a Hedges's g of 0.78 with a 95% confidence interval (CI) of 0.49–1.07. Importantly, subgroup analyses showed that studies with higher methodological quality scores, as assessed by the CRED‐nf checklist, had significantly larger effect sizes (Hedges's g = 1.07) compared to lower than median studies (Hedges's g = 0.49). This finding highlights the importance of addressing key methodological gaps, such as reporting on participant strategies, data processing methods, and the relationship between regulation success and behavioral outcomes. In conclusion, NFT showcases a moderate positive impact on sport motor task, particularly when high‐quality methodologies are employed, as assessed by the CRED‐nf checklist, underscoring the importance of rigorous study designs in future research.

Introduction: Achieving optimal visuomotor performance in precision sports relies on maintaining an optimal psychological state during motor preparation. To uncover the optimal psychological state, extensive EEG studies have established a link between the Mu rhythm (8–13 Hz at Cz) and cognitive resource allocation during visuomotor tasks (i.e., golf or shooting). In addition, the new approach in EEG neurofeedback training (NFT), called the function-specific instruction (FSI) approach, for sports involves providing function-directed verbal instructions to assist individuals to control specific EEG parameters and align them with targeted brain activity features. While this approach was initially hypothesized to aid individuals in attaining a particular mental state during NFT, the impact of EEG-NFT involving Mu rhythm on visuomotor performance, especially when contrasting the traditional instruction (TI) approach with the FSI approach, underscores the necessity for additional exploration. Hence, the objective of this study is to investigate the impact of the FSI approach on modulating Mu rhythm through EEG-NFT in the context of visuomotor performance.Methods: Thirty novice participants were recruited and divided into three groups: function-specific instruction (FSI, four females, six males; mean age = 27.00 ± 7.13), traditional instruction (TI, five females, five males; mean age = 27.00 ± 3.88), and sham control (SC, five females, five males; mean age = 27.80 ± 5.34). These groups engaged in a single-session EEG-NFT and performed golf putting tasks both before and after the EEG-NFT.Results: The results showed that within the FSI group, single-session NFT with augmented Mu power led to a significant decrease in putting performance (p = 0.013). Furthermore, we noted a marginal significance indicating a slight increase in Mu power and a reduction in the subjective sensation of action control following EEG-NFT (p = 0.119). While there was a positive correlation between Mu power and mean radial error in golf putting performance (p = 0.043), it is important to interpret this relationship cautiously in the context of reduced accuracy in golf putting.Discussion: The findings emphasize the necessity for extended investigation to attain a more profound comprehension of the nuanced significance of Mu power in visuomotor performance. The study highlights the potential effectiveness of the FSI approach in EEG-NFT and in enhancing visuomotor performance, but it also emphasizes the potential impact of skill level and attentional control, particularly in complex visuomotor tasks.