{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:32:56Z","timestamp":1790620376854,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Visvesvaraya PhD scheme for Ele","award":["PhD-02\/2022\/57"],"award-info":[{"award-number":["PhD-02\/2022\/57"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a progressive neurodegenerative disorder that affects both motor and cognitive functions, often resulting in misdiagnosis during its early stages. The condition severely impacts daily living, diminishing an individual\u2019s ability to work and carry out routine tasks independently. Consequently, the development of automated methods for reliable PD detection has gained growing research interest. Among the available approaches, Electroencephalography (EEG) has emerged as a promising non-invasive and cost-effective tool. Nevertheless, most existing studies have predominantly focused on resting-state EEG, which constrains the generalizability and robustness of the proposed detection models. This study introduces a cross-stimulation evaluation framework to assess its impact on Parkinson\u2019s disease detection algorithms and conducts channel-wise analysis to identify the most discriminative brain regions for accurate diagnosis. To support this research, we present the newly introduced Parkinson\u2019s disease EEG (ParEEG) database, comprising 203,520 EEG samples from 60 subjects recorded based on Resting-State Visual Evoked Potential (RSVEP) and Steady-State Visually Evoked Potential (SSVEP) stimuli. In this study, we evaluate the performance of individual EEG channels using two handcrafted and two deep learning-based methods, employing a 10-fold cross-validation strategy, to ensure statistical reliability and establish benchmark results. Experimental results show that CRC and LSTM consistently achieved high accuracies (95\u2013100%) with low variability (standard deviation &lt; 2%). The analysis indicates that EEG channels in the frontal, fronto-central, and central\u2013parietal regions consistently yield higher classification accuracy in Parkinson\u2019s disease detection. Our findings offer valuable insights into channel-specific neural alterations for better interpretability in PD, and the cross-stimulation evaluation enhances the generalizability of EEG-based PD detection for practical diagnostic purposes.<\/jats:p>","DOI":"10.3390\/bioengineering12111185","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T01:23:59Z","timestamp":1761873839000},"page":"1185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Stimulus-Evoked Brain Signals for Parkinson\u2019s Detection: A Comprehensive Benchmark Performance Analysis on Cross-Stimulation and Channel-Wise Experiments"],"prefix":"10.3390","volume":"12","author":[{"given":"Krishna","family":"Patel","sequence":"first","affiliation":[{"name":"School of Physical and Applied Sciences, Goa University, Taleigao Plateau 403206, Goa, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajendra","family":"Gad","sequence":"additional","affiliation":[{"name":"School of Physical and Applied Sciences, Goa University, Taleigao Plateau 403206, Goa, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marissa","family":"Lourdes de Ataide","sequence":"additional","affiliation":[{"name":"School of Physical and Applied Sciences, Goa University, Taleigao Plateau 403206, Goa, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6921-6163","authenticated-orcid":false,"given":"Narayan","family":"Vetrekar","sequence":"additional","affiliation":[{"name":"School of Physical and Applied Sciences, Goa University, Taleigao Plateau 403206, Goa, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teresa","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Neurology Department, Goa Medical College and Hospital, Bambolim 403202, Goa, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0484-3956","authenticated-orcid":false,"given":"Raghavendra","family":"Ramachandra","sequence":"additional","affiliation":[{"name":"Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), 7491 Gj\u00f8vik, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100121","DOI":"10.1016\/j.clinsp.2022.100121","article-title":"Sex differences in Parkinson\u2019s Disease: An emerging health question","volume":"77","author":"Vizcaino","year":"2022","journal-title":"Clinics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1002\/mdc3.13584","article-title":"Gender differences in the prevalence of Parkinson\u2019s disease","volume":"10","author":"Zirra","year":"2023","journal-title":"Mov. 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