{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T20:02:32Z","timestamp":1769198552341,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Objective: The aim of this study was to examine how transcranial electrical stimulation (tES) modulates intermuscular coherence (IMC) in sprinters and develop an interpretable neural network model for performance prediction. Methods: Thirty elite sprinters completed a randomized crossover trial involving three tES conditions: motor cortex stimulation (C1\/C2), prefrontal stimulation (F3), and sham. Sprint performance metrics (0\u2013100 m phase analysis) and lower-limb sEMG signals were collected. A Kolmogorov\u2013Arnold Network (KAN) was trained to decode neuromuscular coordination\u2013sprint performance relationships using IMC and time\u2013frequency sEMG features. Results: Motor cortex tDCS increased 30\u201360 m sprint velocity by 2.2% versus sham (p &lt; 0.05, \u03b72 = 0.25). \u03b3-band IMC in key muscle pairs (rectus femoris\u2013biceps femoris, tibialis anterior\u2013gastrocnemius) significantly heightened under motor cortex stimulation (F &gt; 4.2, p &lt; 0.03). The KAN model achieved high predictive accuracy (R2 = 0.83) through cross-validation, with derived symbolic equations mapping neuromuscular features to performance. Conclusions: Targeted tDCS enhances neuromuscular coordination and sprint velocity, while KAN provides a transparent framework for performance modeling in elite sports.<\/jats:p>","DOI":"10.3390\/s25196241","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T07:56:29Z","timestamp":1759996589000},"page":"6241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Effects of Transcranial Electrical Stimulation on Intermuscular Coherence in WuShu Sprint and KAN-Based EMG\u2013Performance Function Fitting"],"prefix":"10.3390","volume":"25","author":[{"given":"Lan","family":"Li","sequence":"first","affiliation":[{"name":"Physical Education Department, Woosuk University, Wanju-gun 55338, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianqian","family":"Fan","sequence":"additional","affiliation":[{"name":"Physical Education Department, Woosuk University, Wanju-gun 55338, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1109\/TNSRE.2023.3242911","article-title":"Surface Electromyography-Based Analysis of the Lower Limb Muscle Network and Muscle Synergies at Various Gait Speeds","volume":"31","author":"Liang","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kerkman, J.N., Bekius, A., Boonstra, T.W., Daffertshofer, A., and Dominici, N. (2020). Muscle Synergies and Coherence Networks Reflect Different Modes of Coordination During Walking. Front. Physiol., 11.","DOI":"10.3389\/fphys.2020.00751"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20220279","DOI":"10.1515\/tnsci-2022-0279","article-title":"Translation of surface electromyography to clinical and motor rehabilitation applications: The need for new clinical figures","volume":"14","author":"Merletti","year":"2023","journal-title":"Transl. Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1007\/s00221-023-06706-6","article-title":"Muscle synergies are associated with intermuscular coherence and cortico-synergy coherence in an isometric upper limb task","volume":"241","author":"Byblow","year":"2023","journal-title":"Exp. Brain Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.clinph.2022.04.022","article-title":"Transcranial magnetic stimulation of the brain: What is stimulated?\u2014A consensus and critical position paper","volume":"140","author":"Siebner","year":"2022","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101738","DOI":"10.1016\/j.arr.2022.101738","article-title":"The effects of aerobic exercise and transcranial direct current stimulation on cognitive function in older adults with and without cognitive impairment: A systematic review and meta-analysis","volume":"81","author":"Talar","year":"2022","journal-title":"Ageing Res. Rev."},{"key":"ref_7","first-page":"461","article-title":"Physical therapy interventions for the management of hand tremors in patients with Parkinson\u2019s disease: A systematic review","volume":"44","author":"Shahien","year":"2023","journal-title":"Neurol. Sci. Off. J. Ital. Neurol. Soc. Ital. Soc. Clin. Neurophysiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"066008","DOI":"10.1088\/1741-2552\/ad017c","article-title":"3D muscle networks based on vibrational mechanomyography","volume":"20","author":"Atashzar","year":"2023","journal-title":"J. Neural Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.neuroimage.2013.11.015","article-title":"A computational modelling study of transcranial direct current stimulation montages used in depression","volume":"87","author":"Bai","year":"2014","journal-title":"NeuroImage"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1016\/S1388-2457(02)00223-7","article-title":"EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: Physiological principles and clinical applications","volume":"113","author":"Grosse","year":"2002","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/S0140-6736(21)00794-7","article-title":"Neuromodulation for chronic pain","volume":"397","author":"Knotkova","year":"2021","journal-title":"Lancet"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1161\/STROKEAHA.121.034200","article-title":"Transcranial Direct Current Stimulation Enhances Neuroplasticity and Accelerates Motor Recovery in a Stroke Mouse Model","volume":"53","author":"Longo","year":"2022","journal-title":"Stroke"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1177\/15459683211017509","article-title":"Effects of Combined and Alone Transcranial Motor Cortex Stimulation and Mirror Therapy in Phantom Limb Pain: A Randomized Factorial Trial","volume":"35","author":"Gunduz","year":"2021","journal-title":"Neurorehabilit. Neural Repair"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1093\/pm\/pnab313","article-title":"Effect of Transcranial Direct Current Brain Stimulation of the Motor Cortex on Chemotherapy-Induced Nausea and Vomiting in Female Patients with Breast Cancer","volume":"23","author":"Kamal","year":"2022","journal-title":"Pain Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1152\/japplphysiol.00145.2023","article-title":"Brain and muscle activity during fatiguing maximum-speed knee movement","volume":"136","author":"Correia","year":"2024","journal-title":"J. Appl. Physiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tun, N.N., Sanuki, F., and Iramina, K. (2021). Electroencephalogram-Electromyogram Functional Coupling and Delay Time Change Based on Motor Task Performance. Sensors, 21.","DOI":"10.3390\/s21134380"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peng, J., Zikereya, T., Shao, Z., and Shi, K. (2024). The neuromechanical of Beta-band corticomuscular coupling within the human motor system. Front. Neurosci., 18.","DOI":"10.3389\/fnins.2024.1441002"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110431","DOI":"10.1016\/j.jneumeth.2025.110431","article-title":"Neuromuscular information transmission patterns for human motor identification on non-invasive tFUS brain signal","volume":"418","author":"Zhu","year":"2025","journal-title":"J. Neurosci. Methods"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.3934\/mbe.2021185","article-title":"Back propagation neural network model for medical expenses in patients with breast cancer","volume":"18","author":"Ruan","year":"2021","journal-title":"Math. Biosci. Eng. MBE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TMI.2022.3218745","article-title":"BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks","volume":"42","author":"Cui","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, G., Ye, J., Hao, H., and Chen, W. (2025). A KAN-based hybrid deep neural networks for accurate identification of transcription factor binding sites. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0322978"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"240999","DOI":"10.1098\/rsos.240999","article-title":"KAN-EEG: Towards replacing backbone-MLP for an effective seizure detection system","volume":"12","author":"Cui","year":"2025","journal-title":"R. Soc. Open Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3663","DOI":"10.1109\/TNNLS.2022.3195821","article-title":"Bisection Neural Network Toward Reconfigurable Hardware Implementation","volume":"35","author":"Chen","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dlugas, H., and Kim, S. (2025). A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics. Metabolites, 15.","DOI":"10.3390\/metabo15030174"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:50:44Z","timestamp":1760035844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,9]]},"references-count":24,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["s25196241"],"URL":"https:\/\/doi.org\/10.3390\/s25196241","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,9]]}}}