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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Sports Sci Med Rehabil . 2026 Feb 26;18:188. doi: 10.1186/s13102-026-01626-8 Search in PMC Search in PubMed View in NLM Catalog Add to search The effect of neurofeedback and EMG biofeedback on gastrocnemius, tibialis anterior, and peroneus longus time to peak EMG in elite athletes with chronic ankle instability: a randomized controlled trial MohammadBagher Shamsi MohammadBagher Shamsi 1 Department of Physiotherapy, School of Rehabilitation Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran Find articles by MohammadBagher Shamsi 1 , Farzaneh Gandomi Farzaneh Gandomi 2 Department of Sport Injuries and Corrective Exercises, Sport Sciences Faculty, Razi University, Kermanshah, Iran Find articles by Farzaneh Gandomi 2, ✉ , Maryam Mirzaei Maryam Mirzaei 1 Department of Physiotherapy, School of Rehabilitation Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran Find articles by Maryam Mirzaei 1, ✉ , Mehrdad Karami Siasiahi Mehrdad Karami Siasiahi 3 Master of Arts (M.A.) in General Psychology, Islamic Azad University, Eslamabad-e-Gharb Branch, Kermanshah, Iran Find articles by Mehrdad Karami Siasiahi 3 Author information Article notes Copyright and License information 1 Department of Physiotherapy, School of Rehabilitation Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran 2 Department of Sport Injuries and Corrective Exercises, Sport Sciences Faculty, Razi University, Kermanshah, Iran 3 Master of Arts (M.A.) in General Psychology, Islamic Azad University, Eslamabad-e-Gharb Branch, Kermanshah, Iran ✉ Corresponding author. Received 2025 Oct 23; Accepted 2026 Feb 19; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13072601 PMID: 41749390 Abstract Introduction Ankle sprains are among the most common sports injuries and frequently progress to chronic ankle instability (CAI). Individuals with CAI often exhibit delayed reaction times in the peroneus longus and tibialis anterior muscles during mechanisms resembling ankle sprains. Neurofeedback and EMG biofeedback (myofeedback) have been reported to improve reaction time in some contexts. Given this background, we investigated whether a four-week neurofeedback or myofeedback program could improve calf-muscle activation timing in athletes with CAI. Materials and Methods In this single-blinded, three-arm randomized controlled trial with a pretest-posttest and parallel group design, forty-five athletes with CAI (age 15–51 years) were recruited via convenience sampling and randomly allocated to control, Surface electromyography (EMG) biofeedback (myofeedback), or neurofeedback groups using a block randomization method. Training groups completed 12 sessions over four weeks (three sessions/week). During training, electrodes were applied and participants performed isolated ankle movements while receiving online feedback; the control group received no feedback training. Surface EMG recorded activity from the gastrocnemius, tibialis anterior, and peroneus longus during jump-landing and lateral-hopping tasks. A foot-switch provided the temporal reference. Outcomes were time to peak EMG (ms; time from foot-switch trigger to muscle activation) and EMG amplitude. Within-group pre–post changes were assessed with paired t-tests; between-group differences were examined with ANCOVA (baseline as covariate). Analyses were conducted in SPSS v23 with α=0.05. Results Six participants were lost to follow-up, resulting in a final per-protocol sample of 39. Neither the neurofeedback nor the myofeedback interventions produced significant within group changes in time to peak EMG or EMG amplitude for the gastrocnemius, tibialis anterior, or peroneuslongus during jump landing or lateral hopping; the control group also showed no significant pre–post changes (all p > 0.05). Consistent with these findings, between group comparisons of the change scores were non significant for all muscles and tasks (ANCOVA F (2, 35) ranged over = 0.492. to 2.037; all p > 0.05, η² ranged over = 0.028 to 0.107). Conclusion In athletes with CAI, four weeks of EEG neurofeedback or EMG biofeedback did not reduce peri-ankle muscle time to peak EMG during jump-landing or lateral-hopping. These null findings highlight the challenge of applying laboratory feedback to sport-specific neuromuscular control. Future protocols should increase training dose, enhance task specificity, individualize targets, and integrate feedback with dynamic, closed-chain practice to improve transfer. Trial registration https:// www.irct.ir (No. IRCT20190426043377N1); Registration date: 15-09-2019 (retrospectively registered). Keywords: Ankle Injuries, Electromyography, Neurofeedback, Biofeedback Introduction Lateral ankle ligament sprains are common in sports that involve jumping and cutting manoeuvres and impose a substantial socio-economic burden through time-loss from sport and healthcare costs [ 1 ]. Approximately 45–75% of individuals with an initial lateral ankle sprain develop chronic ankle instability (CAI) [ 2 ]. CAI is characterised by recurrent episodes of “giving way” or perceived instability, often accompanied by pain, weakness, restricted range of motion, and long-term impairments in function and quality of life [ 3 , 4 ]. Neuromuscular deficits are a key feature of CAI. Individuals frequently show delayed or reduced activation of stabilising muscles such as the medial gastrocnemius (MG), tibialis anterior (TA), peroneus longus (PL), and proximal muscles including the gluteus medius [ 5 ]. Timely activation of these muscles is crucial for maintaining ankle stability during high-risk tasks (e.g. landings and cutting), and delayed onset latency can increase frontal-plane motion and sprain risk [ 6 , 7 ]. Muscle onset latency reflects the time required for proprioceptive information to reach the central nervous system and for an appropriate motor response to be generated in the lower-leg musculature. Because landing is a dynamic task closely aligned with common injury mechanisms, examining muscle time to peak EMG during jump-landing and hopping tasks is clinically relevant in CAI [ 8 ]. Although muscle onset latency is commonly used to characterize neuromuscular delays in CAI, time to peak EMG provides complementary information regarding the ability of the neuromuscular system to rapidly generate maximal stabilizing activation following ground contact. During high-risk tasks such as jump-landing and lateral hopping, insufficient or delayed ration activation of the ankle-stabilizing muscles may reduce dynamic joint stiffness and impair frontal-plane control during the critical post-contact phase. Time to peak EMG has been proposed as a temporal descriptor of neuromuscular activation during dynamic tasks; however, its direct relationship with functional ankle stability remains to be fully established. Neurofeedback training (NFT) is a non-invasive method that provides real-time information about brain activity to promote self-regulation of neural states. NFT operates by using real-time EEG recordings and principles of operant conditioning: ongoing brain activity is continuously monitored, and participants receive a visual or auditory signal when activity in a target frequency band moves in the desired direction (and/or when unwanted activity decreases). By repeatedly rewarding these desired patterns, NFT is thought to induce neuroplastic changes in the cortical networks that generate the EEG signal, which can subsequently lead to changes in cognition, motor control, and timing. It has been used to improve cognitive and motor performance and to restore altered brain patterns after injury [ 9 – 12 ]. Surface electromyographic biofeedback (sEMG-BFB) similarly provides real-time feedback on muscle activity to facilitate voluntary modulation of neuromuscular activation and has shown benefits in musculoskeletal and neurological rehabilitation [ 12 , 13 ]. sEMG-BFB operates by recording the electrical activity of a target muscle through surface electrodes and converting this signal, typically its amplitude, into real-time visual or auditory feedback (for example, a bar that rises or a tone that becomes louder as activation increases). Using principles of operant conditioning, patients are instructed to modify the feedback signal by selectively increasing or decreasing activation of the target muscle. Through repeated practice with this contingent feedback, patients learn to better recruit and control specific muscles, which can enhance strength, coordination, and neuromuscular control in the context of rehabilitation. Both approaches directly target central or peripheral components of neuromuscular control and, in principle, could be used to modify the timing of muscle activation in individuals with CAI. However, despite evidence that NFT and sEMG-BFB can improve balance, postural control, and activation amplitude in this population and related cohorts [ 13 ], their effects on lower-leg muscle onset latency during sport-like tasks have not been systematically investigated. In this study, we compared a central approach (EEG-based NFT), targeting cortical regulation of motor control, with a peripheral approach (sEMG-BFB), targeting direct neuromuscular activation, in athletes with CAI. We examined whether either intervention could modify EMG time to peak EMG of the MG, TA, and PL during jump-landing and lateral-hopping tasks. We hypothesised that both neurofeedback and sEMG biofeedback would improve time to peak EMG of the MG, TA, and PL during jump-landing and lateral hopping tasks, and that the magnitude and pattern of effects might differ between interventions. Materials and methods Design and setting This single-blinded, three-arm randomized controlled trial with a pretest–posttest parallel group design was conducted from July 2020 to September 2023 on elite athletes with CAI, defined as individuals actively competing at national or international levels, engaged in structured training programs at least five days per week (often with multiple daily sessions), and all of whom were still in active training at the time of recruitment. The Ethics Committee in Kermanshah University of Medical Sciences approved the research process (IR.KUMS.REC.1398.449) and was registered in Iranian Registry of Clinical Trials center (IRCT20190426043377N1). Prior to signing the informed consent form, all participants were fully informed about the study procedures and were free to withdraw from the study at any time they chose to. For participants under the age of 18, informed consent to participate was obtained from their parents or legal guardians in accordance with ethical guideline. One group of athletes was enrolled in sEMG-BFB programs at the Growth and Technology Centre, affiliated with Razi Uneversity in Iran while the other group participated in a private NFT center in Kermanshah, IRAN. Participants Athletes were eligible if they met the following criteria (based on the International Ankle Consortium) [ 14 , 15 ]: a history of at least one significant ankle sprain within the past six months, accompanied by swelling, pain, and at least one day of lost function; at least two episodes of the affected ankle “giving way”; a Cumberland Ankle Instability Tool (CAIT) score < 24; and an Ankle Instability Instrument (AII) score > 5. Acute musculoskeletal injuries of the lower extremities within the past three months were an exclusion criterion in order to minimise the potential influence of ongoing tissue healing, pain, or inflammation on neuromuscular control during the intervention and outcome assessments. After applying these criteria, 70 athletes consisted of elite ones engaged in various sports disciplines, were screened, 25 did not meet the inclusion criteria and were excluded, and 45 participants were randomized. The study was reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines and the flow diagram of CONSORT provides full details of recruitment, exclusions, allocation, follow-up, and analysis (Fig. 1 ). Screening and exclusion counts are also summarized in the Results section for ease of reference. Fig. 1. Open in a new tab CONSORT flowchart illustrating participant recruitment and their progression through each phase of the clinical trial Sample size calculation The study sample consisted of elite athletes aged 15 to 51 years with CAI. The minimum sample size was determined using G*Power software, based on an effect size of 0.80, an alpha level (α) of 0.05, and a statistical power (1-β) of 0.95. A sample size of 36 participants was calculated, referencing the effect of exercise on the time to peak EMG of the leg muscles as reported by Hopkins et al. [ 16 ]. The a priori sample size calculation was based on an anticipated large effect (Cohen’s d = 0.80), derived from previous feedback-based interventions that reported moderate–to–large improvements in balance and EMG amplitude in CAI and related populations over 4–6 weeks of training. We acknowledge that this assumption is optimistic for EMG timing measures and that the trial was therefore powered primarily to detect large between-group differences. Randomization and blinding The allocation sequence was generated and concealed by an independent epidemiologist using Random Number Generator software with fixed block sizes of three. This epidemiologist had no role in participant recruitment or intervention delivery, ensuring independence in the randomisation process. Eligible participants were then randomized in equal-sized blocks into three groups: control ( n = 15), sEMG BFB ( n = 15), and NFT ( n = 15). Due to the nature of the interventions, participant blinding was not feasible; however, the outcome assessor remained blinded throughout the study. Outcome measures Time to peak EMG of leg muscles The participants were instructed to perform two tasks to measure muscle time to peak EMG before and after the interventions: 1- Standing on a step approximately 40 cm in height, jumping, and landing on both feet. 2- Performing a single lateral hop, jumping on one leg while keeping their arms crossed over their chest. The participant performed a jump on the injured leg over a tape at a distance of 30 cm. Surface electromyography (EMG) recording EMG signals were recorded using a Myon 320 system (Myon AG, Switzerland) with pre-gelled, self-adhesive Ag/AgCl surface electrodes. The inter-electrode distance was maintained at 2 cm, and the amplifier had a common mode rejection ratio (CMRR) of 110 dB. Signals were sampled at 1000 Hz and band-pass filtered between 20 and 450 Hz. To minimise skin impedance, the skin was shaved if necessary, lightly abraded, and cleaned with alcohol wipes before electrode placement (Fig. 2 ). Fig. 2. Open in a new tab Surface EMG electrode placement on the medial gastrocnemius, tibialis anterior, and peroneus longus EMG activity was recorded from the MG, TA, and PL of the involved limb. Electrode placement followed SENIAM recommendations [ 17 ]: for MG, electrodes were placed over the most prominent bulge of the muscle belly; for TA, at one-third of the line between the tip of the fibula and the tip of the medial malleolus; and for PL, at 25% of the line between the head of the fibula and the tip of the lateral malleolus. Correct placement was verified by palpation and by visually inspecting EMG activity during isolated, low-intensity contractions of each muscle. Because the primary outcome of interest was the timing (ms) of the EMG peak relative to foot contact, EMG amplitude was not normalized. A footswitch was placed inside the shoe under the sole of the foot to detect the instant of ground contact during landing and hopping tasks. For each trial, we defined a time window from − 100 to + 200 ms around foot contact and identified the sample at which the EMG signal reached its maximum value within this window. The primary temporal variable was the latency from foot contact to this peak EMG value (“time to peak EMG”), expressed in milliseconds. Negative values indicate that the peak EMG occurred before foot contact, whereas positive values indicate that the peak occurred after foot contact. We acknowledge that, unlike classical onset latency derived from threshold-based algorithms, time to peak EMG reflects the timing of maximal activation and may be less sensitive to subtle changes in initial muscle recruitment. Interventions Participants were randomly allocated into three parallel groups: two intervention groups receiving either NFT or sEMG-BFB, and a control group. Participants in the control group did not receive any feedback training (neither sEMG-BFB nor NFT). They were instructed to maintain their usual daily activities and continue their standard sports training routines during the four-week study period. No sham intervention or additional exercises were prescribed for this group. NFT In order to apply the neurofeedback protocol, an 8-channel ProComp Infiniti device (ProComp Infiniti, Procomp2, Thought Technology, Ltd) was used along with Biograph Infiniti software (version 5.0). The protocol was administered by a psychologist with neurofeedback certification. The intervention period consisted of 12 sessions, conducted in a self-regulated manner with a frequency of three sessions per week, each lasting 45 min. During the NFT protocol, participants focused on a visual feedback task involving a flower displayed on a monitor. Participants performed concentric plantar flexion-dorsiflexion, inversion and eversion contractions while viewing the screen. This task operates on the principles of operant conditioning: the opening of the flower served as a positive visual reward, provided only when the participant’s real-time EEG activity moved in the desired direction (e.g., modulation of alpha/beta waves). Conversely, if the brain activity deviated from the target threshold, the flower would begin to close, signaling a lack of reward. This immediate feedback allowed participants to consciously adjust their cognitive and motor strategies to maintain the flower in an open state, thereby facilitating self-regulation of the targeted cortical networks. The stimulation points used were F3 and F4 for the frontal lobe, and P3 and P4 for the parietal lobe. At the start of each session, participants were given a detailed explanation of the procedure to ensure their understanding and compliance. To prevent interference, all metal objects were removed from the participants’ bodies before the session began. Participants were comfortably seated during the training, with the montage performed on the CZ area with their eyes closed. The protocol involved beta inhibition of sixteen to twenty-four Hz and alpha amplification of eight to twelve Hz (Fig. 3 ). The decision to deliver neurofeedback in a seated position using isolated, low-movement tasks was made to minimise extraneous biomechanical and postural variables and to allow participants to focus on cortical self-regulation in a controlled environment. However, we acknowledge that this approach provides limited mechanical and sensory similarity to dynamic, closed-chain tasks such as jump-landing and lateral hopping, which may have constrained transfer to the outcome measures assessed in this study. No structured mental imagery or explicit instructions to simulate landing or hopping tasks during training were provided. Fig. 3. Open in a new tab A sample image of neurofeedback stimulation in study subjects sEMG-BFB An Enraf Nonius biofeedback instrument, along with 2 cm diameter pre-gelled, self-adhesive electrodes, was used to record the electrical signals from the muscles for feedback. The electrodes were placed on the bellies of the MG, TA, and PL muscles. Before electrode placement, the participants’ skin was cleaned with cotton and medical alcohol after shaving to reduce impedance. The electrodes were positioned on the muscles, and the maximum isometric strength of the MG, TA, and PL muscles was recorded by applying resistance to ankle plantar flexion, dorsiflexion, and eversion, respectively. Each contraction set was followed by a one-minute rest before the next set. sEMG-biofeedback was delivered for the gastrocnemius, tibialis anterior and peroneus longus using the same EMG system. All sessions were performed in a seated, non–weight-bearing position, with the hip and knee flexed to approximately 90° and the ankle approximately in the midrange. During training, participants were instructed to: perform isometric contractions against a fixed resistance by attempting to plantar flex, dorsiflex and evert the ankle without visible joint movement, while receiving real-time auditory feedback. The loudness of the tone increased proportionally with the EMG amplitude. Participants were asked to “activate the muscle as much as possible so that the sound becomes louder” during the contraction phase and to reduce the sound as much as possible during relaxation phases. In this context, “self-regulation” referred to the participant’s ability to voluntarily increase and decrease muscle activation in response to the feedback signal. Real-time visual feedback was provided for all three target muscles simultaneously. The primary goal of the intervention was to increase the amplitude of muscle activation. It is important to note that the protocol did not employ specific quantitative targets or thresholds (e.g., a percentage of maximum voluntary isometric contraction, %MVIC). Instead, participants were instructed to qualitatively self-regulate their muscle activity, attempting to maximize the feedback signal based on their perceived effort. No external load or weight-bearing task was used. The choice of electrode placements, training positions (seated, non–weight-bearing tasks), and use of real-time feedback to encourage increased activation was informed by previous sEMG-biofeedback protocols in CAI and related lower-limb populations [ 18 ]. The sEMG-BFB intervention period was carried out for 12 sessions, a frequency of three sessions per week, each lasting 45 min (Fig. 4 ). It is worth noting that the choice to train participants using seated, isolated contraction tasks was based on the goal of minimizing extraneous variables and focusing specifically on neuromuscular control and proprioceptive feedback in a controlled environment. However, we acknowledge that these training conditions may not fully replicate the dynamic and functional movements involved in real-world scenarios for individuals with chronic ankle instability All neurofeedback and sEMG-biofeedback sessions were conducted in seated, non–weight-bearing, isolated contraction tasks, whereas outcome measures were obtained during high-velocity, closed-chain movements. This mismatch between training and testing tasks reduces the ecological validity of the intervention. The use of seated, non–weight-bearing, open-chain contractions was intended to isolate neuromuscular activation of the target muscles and to reduce variability arising from multi-joint and postural demands. Nevertheless, this training context differs substantially from the dynamic, closed-chain conditions under which EMG outcomes were assessed, potentially limiting the ecological validity and transfer of training effects. Participants were not instructed to use mental imagery or to cognitively link the biofeedback tasks to sport-specific landing or hopping movements. Fig. 4. Open in a new tab Schematic illustration of the sEMG-BFB setup (Image generated by the authors using AI for illustrative purposes only) Statistical analysis Statistical data analysis was performed using IBM SPSS Statistics software version 23 and a p-value of less than 0.05 was considered significant. Kolmogorov-Smirnov test was used to determine the normality of the distribution. Additionally, to assess the differences between the groups at baseline, an analysis of variance (ANOVA) or Chi-square test was used, as appropriate. Paired samples t-test was used for explore within-group changes. Between-group differences in change scores (post–pre) were examined using analysis of covariance (ANCOVA), with baseline values entered as covariates. This approach is recommended for randomised controlled trials with baseline and follow-up measurements, as it increases statistical power and accounts for any baseline imbalance [ 19 ]. The assumption of homogeneity of regression slopes was tested and met (group × baseline interaction: F range = 0.032 to 0.704, all p > 0.05). Other ANCOVA assumptions (linearity between covariate and outcome, normality of residuals, and homogeneity of variances) were assessed and found to be satisfied. Results As shown in the study flowchart (Fig. 1 ), six participants (four in the sEMG-BFB group and two in the NFT group) did not complete the trial for personal reasons and were excluded for missing more than two sessions; therefore, the final per-protocol analysis included 39 participants. The baseline characteristics of the 13 participants in the NFT group, 11 participants in the sEMG-BFB group, and 15 participants in the control group are presented in Table 1 . There were no statistically significant differences among the three groups in terms of demographic variables (all p > 0.05). Table 1. Comparison of baseline demographic and background characteristics between groups Characteristics NFT group ( n = 13) sEMG-BFB group ( n = 11) Control ( n = 15) p -value Age (year) 20.308 (4.516) 22.273 (7.617) 22.867 (8.37) 0.621 # Height (cm) 167.615 (21.046) 172.182 (8.471) 180.333 (13.37) 0.101 # Weight (kg) 64.231 (11.741) 70.182 (12.936) 71.000 (16.30) 0.406 # Leg length (cm) 93.077 (8.873) 90.182 (5.528) 93.428 (8.68) 0.561 # Sex Female 8 (61.50) 5 (45.50) 7 (46.70) 0.662* Male 5 (38.50) 6 (54.50) 8 (53.30) Open in a new tab Data are means (SD) except sex that presented as number (percent)/ # Based on on-way ANOVA test/ * Based on Fisher’s exact tests/ NFT: Neurofeedback training; sEMG-BFB: Surface electromyographic biofeedback Comparisons of all variables between the intervention and control groups are presented in Tables 2 and 3 ). Table 2. Comparison of time to peak EMG of leg muscles in jump-landing among the 3 groups of the study Muscles Measurement period NFT group ( n = 13) sEMG-BFB group ( n = 11) Control ( n = 15) # P value between groups Medial gastrocnemius (MG) Baseline -0.041(0.196) -0.027(0.122) -0.028(0.212) P-value = 0.527; F = 0.654; df = 2, 35, η²= 0.037 After trial -0.019(0.131) 0.046(0.166) -0.012(0.155) Mean Change (95% CI) 0.022 (-0.137 to 0.181) 0.073(-0.074 to 0.219) 0.007(-0.153 to 0.167) *P value (within groups) 0.769 0.297 0.925 Tibialis anterior (TA) Baseline 0.021(0.185) 0.053(0.113) 0.048(0.187) P value = 0.533; F = 0.641; df = 2, 35; η²= 0.036 After trial 0.044(0.104) 0.096(0.117) 0.061(0.133) Mean Change (95% CI) 0.023(-0.117 to 0.163) 0.043(-0.061 to 0.146) -0.0001(-0.147 to 0.146) *P value (within groups) 0.728 0.378 0.997 peroneus longus (PL) Baseline 0.003(0.183) 0.044(0.097) 0.039(0.221) P value = 0.146; F = 2.037; df = 2, 35; η²= 0.107 After trial 0.033(0.118) 0.142(0.204) 0.037(0.135) Mean Change (95% CI) 0.031(-0.117 to 0.178) 0.099(-0.061 to 0.258) -0.017(-0.184 to 0.149) *P value (within groups) 0.662 0.199 0.829 Open in a new tab Mean (Standard deviation) and Effect size (η²), Mean Change and 95% confidence interval were reported/ # P value is reported based on the analysis of covariance/ *P value is reported based on the paired t-test/ F-value is the ratio of two variances, or technically, two mean squares; df: degrees of freedom. Negative values mean that the muscle contraction precedes the footswitch signal Table 3. Comparison of time to peak EMG of leg muscles in lateral hopping among the 3 groups of the study Muscles Measurement period NFT group ( n = 13) sEMG-BFB group ( n = 11) Control ( n = 15) # P value between groups Medial gastrocnemius (MG) Baseline 0.051(0.108) 0.015(0.137) 0.021(0.109) P-value = 0.616; F = 0.492; df = 2, 35; η²= 0.028 After trial 0.032(0.128) 0.065(0.080) 0.047(0.086) Mean Change (95% CI) -0.019(-0.119 to 0.082) 0.051(-0.031 to 0.134) 0.024(-0.045 to 0.093) *P value (within groups) 0.687 0.193 0.462 Tibialis anterior (TA) Baseline 0.030(0.087) 0.049(0.125) 0.044(0.079) P value = 0.331; F = 1.142; df = 2, 35; η²= 0.063 After trial 0.014 (0.118) 0.060 (0.101) 0.002(0.063) Mean Change (95% CI) -0.017(-0.109 to 0.076) 0.011(-0.059 to 0.082) -0.042(-0.100 to 0.071) *P value (within groups) 0.705 0.728 0.150 Peroneus longus (PL) Baseline 0.055(0.106) 0.020 (0.054) 0.066(0.136) P value = 0.503; F = 0.701; df = 2, 35; η²= 0.040 After trial 0.033(0.131) 0.071(0.102) 0.022(0.084) Mean Change (95% CI) -0.023(-0.116 to 0.069) 0.051(-0.033 to 0.135) -0.04(-0.139 to 0.053) *P value (within groups) 0.597 0.205 0.349 Open in a new tab Mean (Standard deviation) and Effect size (η²), Mean Change and 95% confidence interval were reported/ # P value is reported based on the analysis of covariance/ *P value is reported based on the paired t-test/F-value is the ratio of two variances, or technically, two mean squares; df: degrees of freedom. Negative values mean that the muscle contraction precedes the footswitch signal After adjusted for baseline measurements, ANCOVA revealed no significant between-group difference for MG (F (2, 35) = 0.654, p = 0.527, η² = 0.037), TA (F (2,35) = 0.641, p = 0.533, η² = 0.036) and PL (F (2, 35) = 2.037, p = 0.146, η² = 0.107) during jump-landing. Also, during lateral hopping, ANCOVA adjusting for baseline showed no significant effect of group for the MG (F (2, 35) = 0.492, p = 0.616, η² = 0.028), TA (F (2, 35) = 1.142, p = 0.331, η² = 0.063) and PL (F (2, 35) = 0.701, p = 0.503, η² = 0.040). Additionally, changes over time in all study variables—including EMG time to peak EMG of muscles during jump-landing and lateral hopping—were not statistically significant within the intervention or control groups (paired t-test, all p > 0.05). Discussion The primary objective of this study was to compare the effects of NFT and sEMG-BFB on the time to peak EMG of the MG, TA, and PL muscles during jump landing and lateral hopping in elite athletes with CAI. Contrary to our hypothesis, the results indicated no significant between-group differences for any of the outcome measures. Specifically, neither the NFT nor the sEMG-BFB interventions led to significant improvements in time to peak EMG compared to the control group across both functional tasks. This study possesses several notable strengths. First, the randomised controlled trial design enhances internal validity and minimises potential sources of bias. Second, the methodological rigour is reflected in the use of standardised protocols for both neurofeedback and EMG biofeedback interventions, alongside clearly defined and systematically measured outcome variables. Finally, the clinical relevance of the work is underscored by its focus on neuromuscular timing in elite athletes with CAI, a population for whom the temporal organisation of muscle activity represents a critical and practically significant outcome. However, neither between-group nor within-group analyses revealed significant changes in time to peak EMG for any muscle in either task. A parsimonious interpretation of our findings is that, under the present conditions, the specific EEG-based neurofeedback and EMG biofeedback protocols used in this study do not meaningfully influence EMG peak timing in athletes with CAI, or that any true effects are smaller than our study was powered to detect. In addition, methodological limitations – including the relatively small sample size and the limited sensitivity and reliability of surface-EMG time-to-peak analysis – may have obscured subtle changes in timing. Individuals with CAI frequently exhibit atypical neuromuscular patterns—delayed or premature activation, altered contraction magnitude, and compensatory recruitment—that perpetuate joint instability and elevate the risk of recurrent sprains [ 20 , 21 ]. Jump-landing and lateral hopping demand rapid, precisely coordinated activation to preserve ankle stability; disruption of this coordination increases injury risk [ 20 , 21 ]. The TA contributes to dorsiflexion and controlled initial contact, while the peroneal muscles furnish dynamic lateral stability [ 16 ]. Delays or alterations in these muscles’ activation can reduce control of frontal-plane motion, heighten sprain susceptibility, and contribute to “giving-way” sensations [ 22 ]. Such timing deficits likely reflect altered central control; ligament injury can provoke neuroplastic changes—at cortical and supraspinal levels—that sometimes become maladaptive [ 23 ]. A review of brain neuroplasticity related to lateral ankle ligamentous injuries demonstrated structural and functional brain adaptations in individuals with lateral ankle sprains and CAI compared with healthy controls or those with motor disabilities. These adaptations were associated with clinical outcomes (e.g. self-reported function and differential clinical assessments) and may contribute to the persistence of impairments, increased risk of reinjury, and long-term complications in this population [ 24 ]. On this basis, we employed neurofeedback to recalibrate central activity with the aim of accelerating lower-leg reaction times [ 25 ]. Despite this rationale, the present four-week neurofeedback protocol did not yield detectable improvements in time to peak EMG. One plausible explanation relates to the afferent–efferent loop and sensorimotor integration requirements of the outcome tasks. The timing of muscle activation during landing and hopping depends on rapid integration of proprioceptive, visual, and vestibular inputs and the generation of an appropriate motor command at spinal and supraspinal levels. Most neurofeedback in our study was delivered in seated, low-movement contexts that emphasize afferent processing of visual EEG feedback while under-engaging the efferent drive, load-dependent spindle activity, and postural control demands that are recruited during dynamic tasks [ 26 ]. We chose seated and isolated contraction tasks to minimize extraneous variables and focus on neuromuscular control in a controlled environment; however, these training conditions do not fully replicate the multisensory and mechanical demands of real-world landings. This mismatch is particularly relevant because jump-landing relies heavily on the stretch–shortening cycle and high-load eccentric–concentric transitions, which produce EMG activation patterns that differ from those observed during isolated contractions. Consistent with this, Padulo et al. reported that neuromuscular activation profiles vary across contraction modes and that SSC conditions elicit distinct activation demands compared with isolated concentric or eccentric actions [ 27 ]. Therefore, adaptations gained under seated, isolated training may have limited transfer to the SSC-dominant landing and hopping tasks assessed here. Thus, the sensory and motor context of training may not have been sufficiently similar to the test tasks to promote transfer to the neuromuscular control processes reflected in time to peak EMG during jump-landing and lateral hopping. Our findings align with reports that neurofeedback can improve cortical self-regulation without consistent transfer to dynamic postural or performance measures in athletic populations [ 28 , 29 ]. The findings of the review by Skalski et al. also align with the present study, stating that although there is encouraging evidence for the potential effectiveness of real-time EEG neurofeedback to enhance cognitive, mental, and motor performance in elite athletes, considerable heterogeneity has been observed, highlighting the importance of protocol personalization and methodological standardization [ 30 ]. Collectively, these data suggest that neurofeedback alone may be insufficient to alter millisecond-scale activation timing unless paired with task-specific, movement-rich training that engages the same sensorimotor integration processes as the target tasks. From a motor learning perspective, the principle of specificity suggests that practice effects are greatest when the training conditions closely match the target task in terms of movement pattern, sensory cues, and environmental constraints. In our protocol, neurofeedback practice consisted mainly of relatively simple, predictable tasks with limited postural demand, whereas the outcome measures involved high-velocity, closed-chain landings and hops. This limited task specificity may have constrained transfer to the postural tests used in the study. Consistent with this view, Gołaś et al. (2024) showed that neuromuscular control during the bench press is strongly conditioned by the loading modality (free weights vs. pneumatic resistance) and external load, with distinct EMG activation profiles across conditions, underscoring that EMG activation patterns are highly task- and load-specific [ 31 ]. These findings support our interpretation that neuromuscular adaptations achieved during isolated biofeedback tasks may not generalise to more complex, dynamic jump-landing tasks unless the training closely reproduces their mechanical and sensorimotor demands. In addition, the structure of practice likely involved relatively low contextual interference (e.g. repeated performance of similar exercises in a blocked or highly predictable fashion). Although such conditions can support short-term performance improvements, higher contextual interference—through variable practice and randomised task order—is generally associated with more robust learning and better transfer to novel or more complex tasks. The combination of modest task specificity and low contextual interference may therefore have limited the extent to which neurofeedback-induced changes in cortical regulation, if present, translated into altered EMG peak timing during dynamic landings and hops. Similarly, EMG biofeedback did not change time to peak EMG in the gastrocnemius, TA, or PL over four weeks. Several studies reporting benefits from EMG biofeedback in CAI or related cohorts have focused on activation magnitude (e.g. increases in TA and peroneal activity) or balance/postural outcomes rather than timing per se [ 32 , 33 ]. For example, Kim et al. observed superior improvements in % maximal voluntary isometric contraction (%MVIC) of the TA, tibialis posterior, and peroneal muscles when biofeedback was incorporated into ankle-stabilisation exercises [ 18 ]. This distinction—amplitude or balance versus temporal parameters such as time to peak EMG—may partly explain why our results diverge from prior positive findings. Taken together, these findings suggest that neuromuscular adaptations in the amplitude domain (e.g. increased recruitment or firing rates) can occur without concomitant changes in temporal parameters such as time to peak EMG. In other words, the systems governing “how much” the muscle activates and “when” peak activation occurs may adapt partly independently, which provides a plausible explanation for why biofeedback-based interventions can improve EMG amplitude or balance while leaving peak timing essentially unchanged. Consistent with previous cautions, EMG biofeedback may not induce durable motor learning unless embedded within functional, closed-chain, and progressively overloaded tasks that closely resemble the target performance [ 34 , 35 ]. Evidence from other regions (e.g. hip abductors) similarly suggests that neural feedback without dynamic, multi-joint practice shows limited carry-over to strength or coordination [ 36 , 37 ]. Again, in terms of motor learning principles, the relatively low contextual interference and limited specificity of our EMG biofeedback tasks may have constrained adaptation of the complex, anticipatory activation patterns required during landing and hopping. Heterogeneity in protocols likely contributes to discrepant results across studies. Neurofeedback paradigms vary in targeted frequency bands, schedule, and progression; EMG biofeedback protocols differ in task specificity, intensity, and feedback rules. Some investigations report gains in postural control or pain modulation with longer or more intensive regimens [ 38 – 41 ]. By contrast, our four-week dose may have been too brief or insufficiently specific to remodel the timing of rapid, sport-like actions. Clinical implications Taken together, our findings suggest that neither seated neurofeedback nor stand-alone EMG biofeedback is likely to accelerate ankle-stabilizing muscle time to peak EMG in athletes with CAI without concurrent, context-relevant practice. Clinicians aiming to modify timing should consider integrating feedback with perturbation-based, closed-chain drills that progressively load the ankle and replicate sport-specific constraints. Future directions Future protocols should increase training dose and duration beyond four weeks to allow sufficient repetition for neuromuscular adaptation. Interventions should enhance task specificity by incorporating exercises that closely replicate the mechanical and sensorimotor demands of jump-landing and lateral hopping. Neurofeedback and sEMG-BFB parameters should be individualised, with participant-specific targets, thresholds, and progression criteria based on baseline neuromuscular profiles. Feedback-based training should incorporate graded progression and greater practice variability (e.g. variable/perturbed or randomised task conditions) to strengthen motor learning and improve transfer to sport-specific performance. Limitations Several limitations should be considered when interpreting the findings of this study. First, regarding the EMG analysis, our primary temporal outcome was time to peak EMG relative to foot contact rather than classical onset latency derived from threshold-based detection algorithms. Although this measure reflects the timing of maximal stabilizing activation, it is less sensitive to subtle changes in the initial recruitment of muscle activity and is more susceptible to variability in the EMG envelope shape. Consequently, small adaptations in early neuromuscular timing may not have been detected. Second, the absence of a healthy control group limits the interpretation of the baseline “deficit.” Because no direct comparison with robust normative EMG timing values was possible, it remains uncertain whether the baseline timing patterns observed in our CAI athletes were truly pathological or relatively subtle. This ambiguity complicates the interpretation of the null findings regarding the intervention’s effectiveness. Third, the design of the biofeedback protocols presented notable constraints. Neither the neurofeedback nor the sEMG-BFB protocols incorporated explicit quantitative thresholds, target activation ranges, or a formal progression model grounded in motor learning principles such as specificity and contextual interference. Participants were instructed to qualitatively “self-regulate” their brain or muscle activity based on the feedback signal, but without clearly defined criteria for success or systematically increasing task demands. These features likely limited both the magnitude of learning and its transfer to the dynamic landing and hopping tasks assessed in this study. Moreover, all training tasks were performed in seated, non–weight-bearing, isolated conditions, whereas EMG outcomes were assessed during high-velocity, closed-chain jump-landing and lateral-hopping tasks; this lack of task and load specificity reduces ecological validity and may have limited adaptation of neuromuscular timing. Fourth, the intervention period was relatively short (four weeks; 12 sessions) compared with some feedback-based protocols reporting beneficial effects, so the cumulative dose of practice may have been insufficient to induce robust changes in time to peak EMG. Furthermore, the sample size calculation was based on an effect size of 0.80 derived from previous studies focusing on balance and EMG amplitude. We acknowledge that this assumption may not be optimal for detecting changes in EMG timing outcomes. Consequently, the study may have been underpowered to identify smaller, yet potentially meaningful, differences in timing measures, representing a limitation regarding the statistical power of the analysis. Fifth, the sample included a wide age range (15–51 years), with a small number of older athletes (≥ 40 years). While our inclusion criteria specifically targeted individuals with a history of recurrent sprains and mechanical instability characteristic of CAI, participants over the age of 40 are statistically more likely to present with ankle osteoarthritis. Although we excluded individuals with a history of fracture or surgery to minimize this risk, the lack of radiographic examination means we cannot completely rule out degenerative changes in the older participants. This may have increased heterogeneity and potentially influenced the neuromuscular responses observed. Finally, methodological factors such as blinding and attrition should be noted. Participants could not be blinded to group allocation or intervention type (only the outcome assessor was blinded), so performance bias cannot be entirely excluded, although the primary EMG measures were objective and collected using standardised procedures. In addition, in this study, analyses were conducted using a per-protocol approach, which means that only participants who adhered strictly to the study protocol were included in the final analysis. This method can provide insights into the efficacy of the treatment among those who followed the prescribed interventions. However, it also has the potential to introduce bias, as it may overestimate the treatment effects by excluding participants who did not comply with the protocol. Six participants (13%) did not complete the intervention, and differential attrition could have introduced bias and further reduced statistical power. The large confidence intervals observed for time to peak EMG suggest substantial variability and limited precision of this measure in our setting, reinforcing the need for future studies to use validated onset-detection methods and to include formal reliability analyses. Conclusion In athletes with CAI, four weeks of EEG neurofeedback or EMG biofeedback did not reduce peri-ankle muscle time to peak EMG during jump-landing or lateral-hopping. These null findings highlight the challenge of applying laboratory feedback to sport-specific neuromuscular control. Future protocols should increase training dose, enhance task specificity, individualize targets, and integrate feedback with dynamic, closed-chain practice to improve transfer. Acknowledgements The authors thank the Kermanshah University of Medical Sciences for grants to support this research (Code: 980856). They also thank the participants of the study for their time and dedication. Abbreviations ANCOVA Analysis of Covariance ANOVA Analysis of variance AII Ankle Instability Instrument CAI Chronic ankle instability CAIT Cumberland Ankle Instability Tool CMRR Common Mode Rejection Ratio CONSORT Consolidated Standards of Reporting Trials EEG Electroencephalogram EMG Electromyography FI Functional instability PL peroneus longus MI Mechanical instability MG Medial gastrocnemius MVIC Maximal voluntary isometric contraction NFT Neurofeedback training sEMG-BFB Surface electromyographic biofeedback TA Tibialis anterior Authors’ contributions MB.Sh and F.G designed the research and contributed to the conception of the project, development of overall research plan, and study oversight. MB.Sh and F.G were the principal investigators in this project and the contact person responsible for ethical approval. MB.Sh, F.G, M.M and M.KS were involved in sampling and data collection and conducted all outcome measures. MB.Sh and M.M analyzed and interpreted the data. MB.Sh, F.G, M.M and M.KS drafted and revised the manuscript. All authors contributed to parts of the manuscript and have read and approved the final manuscript. Funding Funding was supported by Kermanshah University of Medical Sciences (reference number: 980856). We affirm that we have no financial affiliation (including research funding) or involvement with any commercial organization that has a direct financial interest in any matter included in this manuscript. Data availability The data collected and analyzed in the present study are not publicly available due to ethical restrictions but are available from the corresponding author upon request. Declarations Ethics approval and consent to participate The study was approved by the ethics committee of Kermanshah University of Medical Sciences (Reference number: IR.KUMS.REC.1398.449) and all methods were performed in accordance with the Declaration of Helsinki. All participants in the study gave their informed consent to take part, and for those under 18 years of age, informed consent was obtained from their parents or legal guardians. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Farzaneh Gandomi, Email: [email protected]. Maryam Mirzaei, Email: [email protected]. References 1. Yalfani A, Gandomi F. The comparison of lower and upper extremity muscles activation during sudden ankle supination in patients with and without chronic ankle instability. Med Dello Sport. 2016;69(2):254–66. [ Google Scholar ] 2. Jeon HG, Lee SY, Park SE, Ha S. Ankle instability patients exhibit altered muscle activation of lower extremity and ground reaction force during landing: a systematic review and meta-analysis. 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[ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The data collected and analyzed in the present study are not publicly available due to ethical restrictions but are available from the corresponding author upon request. 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