{"paper_id":"290a08ce-18fe-4f20-9f80-70dbb540576c","body_text":"A Hybrid SHO-PSO Optimized CNN-GRU-MHA with SHAP-Based Channel Selection for Low-Complexity Hardware ADHD Detection | 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 Research Article A Hybrid SHO-PSO Optimized CNN-GRU-MHA with SHAP-Based Channel Selection for Low-Complexity Hardware ADHD Detection NAURIN BAHADUR, Lakshmi Boppana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7805264/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 21 You are reading this latest preprint version Abstract Accurate and efficient hardware based detection of Attention-Deficit/ Hyperactivity Disorder (ADHD) is crucial for portable EEG diagnostics. This study proposes a methodology for designing low-complexity hardware targeting real-time ADHD detection from EEG signals. A hy- brid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) architecture enhanced with a multi-head attention mechanism is proposed for ADHD detection. To optimize model performance and complexity, a hybrid Sea-Horse Optimization (SHO) and Particle Swarm Optimization (PSO) algorithm is proposed to automatically tune critical hyperparameters: the number of GRU units, the number of attention heads, and the key vector dimension per head, achieving an initial accu- racy of 98.34%. Subsequently, to further reduce hardware complexity and latency while improving accuracy, GradientExplainer SHAP (SHapley Additive exPlanations) is applied for eXplainable AI (XAI) to identify and retain only the most significant EEG channels. This channel-pruning approach increases accuracy to 99.22%. The optimized model is also implemented on an Artix-7 FPGA (xc7a100t-csg324) FPGA device and compared with other models. This implementation val- idates the feasibility of the approach and serves as a prototype for developing Application-Specific Integrated Circuit (ASIC) solutions, paving the way for efficient, low-latency wearable ADHD di- agnostic devices. ADHD Disorder Multi-head Attention mechanism CNN-GRU DWT EEG FPGA Sea Horse optimization (SHO) Particle Swarm Optimization (PSO) SHAP Exaplainable AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Feb, 2026 Reviews received at journal 25 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviews received at journal 18 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers invited by journal 16 Nov, 2025 Editor assigned by journal 16 Nov, 2025 Submission checks completed at journal 12 Oct, 2025 First submitted to journal 08 Oct, 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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This study proposes a methodology for designing low-complexity hardware targeting real-time ADHD detection from EEG signals. A hy- brid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) architecture enhanced with a multi-head attention mechanism is proposed for ADHD detection. To optimize model performance and complexity, a hybrid Sea-Horse Optimization (SHO) and Particle Swarm Optimization (PSO) algorithm is proposed to automatically tune critical hyperparameters: the number of GRU units, the number of attention heads, and the key vector dimension per head, achieving an initial accu- racy of 98.34%. Subsequently, to further reduce hardware complexity and latency while improving accuracy, GradientExplainer SHAP (SHapley Additive exPlanations) is applied for eXplainable AI (XAI) to identify and retain only the most significant EEG channels. This channel-pruning approach increases accuracy to 99.22%. The optimized model is also implemented on an Artix-7 FPGA (xc7a100t-csg324) FPGA device and compared with other models. This implementation val- idates the feasibility of the approach and serves as a prototype for developing Application-Specific Integrated Circuit (ASIC) solutions, paving the way for efficient, low-latency wearable ADHD di- agnostic devices.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Hybrid SHO-PSO Optimized CNN-GRU-MHA with SHAP-Based Channel Selection for Low-Complexity Hardware ADHD Detection\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-26 04:26:54\",\"doi\":\"10.21203/rs.3.rs-7805264/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision 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