EEG Classification for Neurological Disorders Using Frequency Band Deciles

EEG Classification for Neurological Disorders Using Frequency Band Deciles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article EEG Classification for Neurological Disorders Using Frequency Band Deciles Jonah Fernandez, Bianca Innocenti, Beatriz López This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7084929/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 22 You are reading this latest preprint version Abstract Electroencephalography (EEG) is a widely used non-invasive technique for monitoring brain activity, offering valuable insights into neurological disorders. Feature extraction methods based on signal processing approaches have been shown to be effective, but they tend to overlook the statistical properties of EEG signals. This study proposes a decile-based feature extraction method for EEG signal analysis, aimed at improving classification performance while maintaining simplicity and interpretability. The method was evaluated across multiple tasks, including the classification of Alzheimer’s disease (AD), frontotemporal dementia (FTD), Parkinson’s disease (PD), and seizure detection, using three machine learning models: Random Forest (RF), K-Nearest Neighbors (KNN), and LightGBM. Experimental results demonstrate that the decile-based approach, particularly when paired with RF and KNN, achieves high classification accuracy. Furthermore, the proposed method showed strong robustness to reduced channel counts, highlighting its potential for application in low-cost, wearable EEG systems. While model performance varied across datasets, particularly for LightGBM, the overall results confirm the effectiveness and generalizability of decile-based features in diverse EEG classification tasks. These findings support the method’s potential for clinical use in early diagnosis and real-time monitoring of neurological conditions, especially in resource-constrained or ambulatory settings. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviews received at journal 26 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 16 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Editor invited by journal 17 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 14 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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