{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:29:07Z","timestamp":1790231347430,"version":"4.1.0"},"reference-count":56,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"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":["Eur J of Neuroscience"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Parkinson's Disease (PD) often results in motor and cognitive impairments, including gait dysfunction, particularly in patients with freezing of gait (FOG). Current detection methods are either subjective or reliant on specialized gait analysis tools. This study aims to develop an objective, data\u2010driven, multi\u2010modal classification model for FOG\u2010specific classification, distinguishing PD patients with FOG (PDFOG+) from those without FOG (PDFOG\u2013) and healthy controls using resting\u2010state EEG signals combined with demographic and clinical variables. For our main analysis, we utilized a dataset of 124 participants: 42 PDFOG+, 41 PDFOG\u2013, and 41 age\u2010matched healthy controls. Features extracted from resting\u2010state EEG and descriptive variables (age, education, disease duration) were used to train a novel Bi\u2010cephalic Self\u2010Attention Model (BiSAM). We tested three modalities: signal\u2010only, descriptive\u2010only, and multi\u2010modal, across different EEG channel subsets (BiSAM\u201063, \u221216, \u22128, and \u22124 for primary analysis). For the main analysis, signal\u2010only (BiSAM\u20104) and descriptive\u2010only models showed limited performance, achieving a maximum accuracy of 55% and 68%, respectively. In contrast, the multi\u2010modal models significantly outperformed both, with BiSAM\u20108 and BiSAM\u20104 achieving the highest classification accuracy of 88%. These results demonstrate the value of integrating EEG with objective descriptive features for robust PDFOG+ classification. This study introduces a multi\u2010modal, attention\u2010based architecture that objectively classifies PDFOG+ using minimal EEG channels and descriptive variables. This approach offers a scalable and efficient alternative to traditional assessments, with potential applications in routine clinical monitoring and early diagnosis of PD\u2010related gait dysfunction.<\/jats:p>","DOI":"10.1111\/ejn.70433","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T03:05:30Z","timestamp":1771556730000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bi\u2010Cephalic Self\u2010Attended Model to Classify Parkinson's Disease Patients With Freezing of Gait"],"prefix":"10.1111","volume":"63","author":[{"given":"Shomoita\u00a0Jahid","family":"Mitin","sequence":"first","affiliation":[{"name":"Biomedical and Translational Sciences University of South Dakota  Vermillion South Dakota USA"},{"name":"Artificial Intelligence Research Lab, Department of Computer Science University of South Dakota  Vermillion South Dakota 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