{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:15:34Z","timestamp":1792790134484,"version":"4.5.0"},"reference-count":57,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Science and Technology Program of Henan Province","award":["221100210500"],"award-info":[{"award-number":["221100210500"]}]},{"name":"Central Government Guiding Local Science and Technology Development Fund Program of Henan Province","award":["Z20221343032"],"award-info":[{"award-number":["Z20221343032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a common neurodegenerative disorder, and accurate identification of PD is critical for clinical diagnosis and disease management. Electroencephalography (EEG) sensors provide reliable real-time brain signal acquisition, making them practical biosensing modalities for PD detection. However, due to their non-stationarity, single time-domain or frequency-domain analysis methods are insufficient to extract robust discriminative features from EEG signals. To address this challenge, we propose a multi-domain feature fusion EEG classification model, termed Multi-Domain Fusion Network (MDF-Net), which jointly integrates temporal, frequency-domain, and wavelet-domain representations for accurate PD recognition. MDF-Net employs a Temporal Attention-enhanced Temporal Convolutional Network (TTCN) to capture temporal dependencies and incorporates an improved 1D Convolutional Neural Network mixer module (Cmix) for multi-channel feature fusion. We constructed an EEG dataset of 415 subjects (289 healthy controls and 126 PD patients). Under 5-CV, the proposed method achieved a classification accuracy of 92.3%, an F1-score of 87.3%, and an AUC of 0.943. Experimental results demonstrate that multi-domain feature fusion effectively improves PD detection performance, and EEG sensor-based analysis shows strong potential for clinical application. This study provides a methodological reference for developing objective, practical computer-aided diagnostic tools for PD.<\/jats:p>","DOI":"10.3390\/s25237189","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T16:31:54Z","timestamp":1764088314000},"page":"7189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["EEG Sensor-Based Parkinson\u2019s Disease Detection Using a Multi-Domain Feature Fusion Network"],"prefix":"10.3390","volume":"25","author":[{"given":"Jinxuan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9545-5443","authenticated-orcid":false,"given":"Hua","family":"Huo","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8291-963X","authenticated-orcid":false,"given":"Shilu","family":"Kang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cantero-Fortiz, Y., and Boada, M. 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