A new EEG neurofeedback training approach in sports: the effects function-specific instruction of Mu rhythm and visuomotor skill performance

Mu down regulation EEG-neurofeedback training combined to motor imagery facilitates early consolidation in a sequential finger tapping task

Objective.Motor imagery (MI) has demonstrated positive effects on motor performance and triggers activation in the motor cortex (MC). EEG-Neurofeedback (EEG-NF) is a neuromodulation technique that provides real-time feedback on one's brain activity, enabling self-regulation of brain states. While there is increasing evidence of humans controlling the activity of various brain networks, including the MC, through EEG-NF, the tangible benefits of this self-regulation on motor performance remain uncertain. This study investigates the potential benefits of EEG-NF training in explicit learning of a sequential movement, in comparison to MI training and to a combined EEG-NF and MI training.Approach.Ninety-one right-handed healthy adults were randomly assigned to one of four groups (a)NF(n= 24), (b)MI(n= 22), (c)MI + NF(n= 23) and (d)control(n= 22). Participants performed a sequential finger tapping task before and after (immediately, 20 min and 24 h) a single 30 min training session. Motor performance, movement speed and event related desynchronization data were analyzed.Main results.MI training led to a better motor performance compared to control condition immediately after training that was sustained at the 20 min retest time point (p= 0.02 and 0.05). In contrast, EEG-NF training alone did not yield better motor performance compared to control condition at any time-point (p> .05). Remarkably, only the combination of both trainings led to superior motor performance 24 h after training in comparison to control group (p= 0.02). Additionally, all experimental groups successfully decreased mu rhythm amplitude throughout most of the training.Significance.Combined EEG-NF and MI training appears particularly promising for enhancing motor consolidation holding the potential to advance rehabilitation approaches.

A single session of sensorimotor rhythm neurofeedback enhances long-game performance in professional golfers

Enhanced Sensorimotor Rhythm activity has been linked to increased automation in motor execution. Although existing research demonstrates the positive effects of SMR neurofeedback training on improving golf putting performance, its influence on golf long-game performance remains unexplored. This study sought to address this gap by involving seventeen professional female golfers (Age =24.63 ± 3.24 years, Handicap＝2.06 ± 1.18) in a crossover-designed experiment incorporating both NFT and a no-training control condition. During the study, participants executed 40 150-yard swings while receiving continuous SMR neurofeedback. Pre- and post-testing included visual analog scales to assess psychological processes associated with SMR activities, including attention engagement, conscious motor control, and physical relaxation levels. The results revealed that a single session of NFT effectively heightened SMR power irrespective of T1 (p = .02) or T2 (p = .03), which was observed with improved swing accuracy compared to the control conditions, particularly in "To Pin" (p = .04, the absolute distance to the hole after the ball comes to a stop). Subjective assessments further indicated that SMR NFT contributed to a sense of ease and tranquility during motor preparation for the golf swing (attention engagement: p = .01, conscious motor control: p = .033, physical relaxation: p = .013), and which offered valuable insights into the potential mechanisms underlying the impact of SMR NFT on long-game performance. Additionally, in such practical applications professional athletes can utilize our single-session neurofeedback protocol to train efficiently and cost-effectively before competitions, thereby enhancing their opportunity to achieve a higher rank.

Evaluating EEG neurofeedback in sport psychology: a systematic review of RCT studies for insights into mechanisms and performance improvement

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.

A longitudinal study of the effect of visuomotor learning on functional brain connectivity

Neural adaptation in the frontoparietal and motor cortex-sensorimotor circuits is crucial for acquiring visuomotor skills. However, the specific nature of highly dynamic neural connectivity in these circuits during the acquisition of visuomotor skills remains unclear. To achieve a more comprehensive understanding of the relationship between acquisition of visuomotor skills and neural connectivity, we used electroencephalographic coherence to capture highly dynamic nature of neural connectivity. We recruited 60 male novices who were randomly assigned to either the experimental group (EG) or the control group (CG). Participants in EG were asked to engage in repeated putting practice, but CG did not engage in golf practice. In addition, we analyzed the connectivity by using 8-13 Hz imaginary inter-site phase coherence in the frontoparietal networks (Fz-P3 and Fz-P4) and the motor cortex-sensorimotor networks (Cz-C3 and Cz-C4) during a golf putting task. To gain a deeper understanding of the dynamic nature of learning trajectories, we compared data at three time points: baseline (T1), 50% improvement from baseline (T2), and 100% improvement from baseline (T3). The results primarily focused on EG, an inverted U-shaped coherence curve was observed in the connectivity of the left motor cortex-sensorimotor circuit, whereas an increase in the connectivity of the right frontoparietal circuit from T2 to T3 was revealed. These results imply that the dynamics of cortico-cortical communication, particularly involving the left motor cortex-sensorimotor and frontal-left parietal circuits. In addition, our findings partially support Hikosaka et al.'s model and provide additional insight into the specific role of these circuits in visuomotor learning.