{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T13:47:15Z","timestamp":1788961635447,"version":"build-2803163510"},"reference-count":49,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CNS Neurosci Ther"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Sleep\u2010related and neuromuscular conditions affect the daily lives of individuals as they impact physical and cognitive well\u2010being. While not classified as a disorder, bruxism has emerged as a prevalent condition characterized by involuntary teeth grinding and jaw clenching, occurring either during sleep or wakefulness. Often left unnoticed, this unconscious behavior can contribute to severe dental damage, facial muscle fatigue, and temporomandibular joint disorders. These consequences require early detection and intervention to prevent long\u2010term complications. Traditionally, polysomnography (PSG) is widely used for bruxism assessments as it gives insights into the multimodal physiological data, but it lacks direct spatial mapping of neural regions involved in rhythmic masticatory muscular activity (RMMA) associated with bruxism.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>This research introduces functional Near Infrared Spectroscopy (fNIRS) as a neuroimaging tool to monitor cortical activity associated with RMMA, distinguishing bruxism from other masticatory activities. The data were acquired in a controlled simulated paradigm setup from 10\u2009subjects in three trials via a 20\u2010channeled optode setup of fNIRS placed over the motor cortex region. A total of 12 temporal and frequency domain features were optimized by employing techniques of feature selection, feature importance, and feature reduction. Furthermore, synthetic data augmentation techniques of Synthetic Minority Oversampling Technique (SMOTE), Synthetic Minority Oversampling Technique for Nominal features (SMOTEN), and Adaptive Synthetic sampling (ADASYN) were compared to five machine learning classifiers including k\u2010Nearest Neighbors (kNN), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The kNN outperformed in detecting simulated bruxism among other mandible joint movements with an accuracy of 92%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings highlight the potential of fNIRS as a tool for identifying and distinguishing bruxism\u2010like motor activities from other jaw movements, contributing to the timely management and detection of bruxism in future studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/cns.70619","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T10:21:47Z","timestamp":1758536507000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence\u2010Driven Hemodynamic Monitoring of Simulated Bruxism Using Functional Near\u2010Infrared Spectroscopy: A Preliminary Study"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8320-4648","authenticated-orcid":false,"given":"Noor","family":"Fatima","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Air University  Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zia Mohy Ud","family":"Din","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Air University  Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Al","family":"Aishan","sequence":"additional","affiliation":[{"name":"Department of Basic Medical Sciences, College of Applied Sciences King Khalid University  Abha Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jahan Zeb","family":"Gul","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering Maynooth University  Maynooth Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13144259"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.13985"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.13603"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17207408"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24165426"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.17219\/dmp\/192824"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.12967"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.13295"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010020\u201064615\u2010y"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines10102666"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.2147\/NSS.S268470"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.17219\/dmp\/183692"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13643\u2010016\u20100397\u2010z"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11940\u2010016\u20100396\u20103"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.17219\/dmp\/191517"},{"key":"e_1_2_10_17_1","volume-title":"StatPearls","author":"Lal S. 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