Adaptive Quantum-Inspired Framework with Meta-Learning for Enhanced ALS Biomarker Discovery and Brain-Computer Interface Applications

Adaptive Quantum-Inspired Framework with Meta-Learning for Enhanced ALS Biomarker Discovery and Brain-Computer Interface Applications | 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 Adaptive Quantum-Inspired Framework with Meta-Learning for Enhanced ALS Biomarker Discovery and Brain-Computer Interface Applications Shubham Chandra¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7513254/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Amyotrophic Lateral Sclerosis (ALS) presents significant challenges for biomarker identification and brain-computer interface (BCI) development due to progressive motor neuron degeneration and high signal-to-noise ratios in neurophysiological recordings. Traditional EEG analysis methods struggle with patient-specific variability and environmental artifacts, limiting their clinical utility. Methods: We present an adaptive hybrid framework integrating dendritic-inspired noise filtering, quantum-inspired symbolic search with Hamming distance optimization, and meta-adaptive machine learning (MAML) for personalized adaptation. The system processes synchronized EEG and eye-tracking data from the EEGET-ALS dataset (45 ALS patients, 42 healthy controls), implementing multiple threshold optimization strategies including Bayesian optimization, evolutionary algorithms, and ensemble methods. The framework maintains a historical performance database enabling continuous learning and transfer learning capabilities across patient cohorts. Results: Our framework achieved an F1-score of 0.967 (95% CI: 0.958-0.976), precision of 0.98, and recall of 0.95 in distinguishing ALS patients from healthy controls (Figure 1o). Key neurophysiological findings include ~25% beta power reduction (13-30 Hz) in central electrodes (Figure 1a-c), 40% motor connectivity loss particularly in C3-C4 coupling (Figure 1e-g), and preserved gamma oscillatory patterns (Figure 1d). The meta-learning module improved performance from baseline F1=0.667 to F1=0.751 within 1000 search iterations (Figure 1h), with transfer learning accelerating convergence by 5-10x (Figure 1l). Conclusions: The quantum-inspired adaptive framework establishes a new paradigm for ALS biomarker discovery and BCI development. By combining biologically-inspired filtering with continuous meta-learning, the system achieves clinical-grade accuracy while adapting to patient-specific variations. This approach enables robust communication interfaces for ALS patients and provides a generalizable framework for neurological biomarker discovery. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Neurology Biological sciences/Neuroscience Amyotrophic Lateral Sclerosis EEG quantum-inspired computing meta-learning braincomputer interface biomarker discovery adaptive filtering Figures Figure 1 Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1Electrodes.pdf Figure1SourceData.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7513254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531907383,"identity":"d0927d4a-1f02-4ca7-8d45-5531ea84dad5","order_by":0,"name":"Shubham Chandra¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJElEQVRIie2QsWqDQBiA/xDQxdDV0IKvYBAMAdFXORFuktKthQZqKNhFMgt9CadAN+WoXWxdLXYpri5ZQgsl7ZlyqKHQdOtw33If3P/B/QfA4fxHZBCGzanuHAwm38QHJHg/Qb8mpJ/AD4lyG+jV2XylTMXHpLyY50xMS7m5fo3f3wwLRHIftYn6kk21MC0nd8GpM8tacewoS9UkQNj2JIyLTiK7+rEklEiNXX3stTJE9ApiCREEsqR3EiVski2dzGs6uX1icmXRK0g+0Ke1l0BBk5FPEyrjhR8zIQOvcIFIKB54/UQt8Lk2WpaTqKi12WLpMHnY7UJOsGP7/V2U0FlV0qZU1NydPHsbk8ll82PVujZM60gkafdhByD8bZzD4XA4AF/6L4eVp98jJgAAAABJRU5ErkJggg==","orcid":"","institution":"¹Chandra Associates","correspondingAuthor":true,"prefix":"","firstName":"Shubham","middleName":"","lastName":"Chandra¹","suffix":""}],"badges":[],"createdAt":"2025-09-02 04:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7513254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7513254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94156725,"identity":"b2bb408a-68ea-4cdd-95a2-b15b35d686b6","added_by":"auto","created_at":"2025-10-23 02:54:37","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":406998,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1ALSAnalysisPreview.png","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/622c9d529979cb4fc2e5c3a3.png"},{"id":94155489,"identity":"9a98a540-4f7f-4d2e-8b63-2f131517def5","added_by":"auto","created_at":"2025-10-23 02:46:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":412215,"visible":true,"origin":"","legend":"","description":"","filename":"CompleteALSEEGETManuscriptforSpringerNaturerev2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/ff62f7c1663373b6bee045ba.pdf"},{"id":94156724,"identity":"f79b4ac6-29f4-469e-a3a5-ab300dadb673","added_by":"auto","created_at":"2025-10-23 02:54:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15796,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1SourceData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/9e92d517cf2b217890ddba8f.xlsx"},{"id":94155485,"identity":"4fdd1ffa-a1da-4992-891b-28834d39814a","added_by":"auto","created_at":"2025-10-23 02:46:37","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4670,"visible":true,"origin":"","legend":"","description":"","filename":"4d86379cfe944fada862d91ef6626c02.json","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/e387d71a5c5d5c28397ad13a.json"},{"id":94155482,"identity":"cf69323e-9953-4198-9a58-b69c6142ff79","added_by":"auto","created_at":"2025-10-23 02:46:37","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27194,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1Electrodes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/57d202b4550a42c562325447.pdf"},{"id":94156723,"identity":"b77d336b-8f2c-42e7-b80c-04da642d021c","added_by":"auto","created_at":"2025-10-23 02:54:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354318,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive ALS EEG-ET biomarker analysis and adaptive framework performance. Multi-panel visualization integrating neurophysiological findings with computational framework results from 45 ALS patients and 42 healthy controls. (a-c) Topographic maps showing beta power (13-30 Hz) distribution in ALS patients (a), healthy controls (b), and difference map (c). Color scale represents power spectral density (μV²/Hz) with warmer colors indicating higher power. Notable ~25% reduction in central electrodes (C3, Cz, C4) in ALS (***p\u0026lt;0.001, FDR-corrected). (d) Spectral power changes across frequency bands (mean ± SEM). Delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-50 Hz). **p\u0026lt;0.01, ***p\u0026lt;0.001 versus controls, Bonferroni-corrected. (e-f) Functional connectivity matrices using phase-locking values (PLV) demonstrating reduced motor network coupling in ALS (e) versus preserved connectivity in controls (f). Color scale: -1.0 (anti-phase) to +1.0 (perfect synchrony). (g) Electrode-wise connectivity comparison showing 40% C3-C4 coupling loss (white box, p\u0026lt;0.001). (h) Dendritic filter convergence over 10 optimization iterations, showing F1 score improvement from 0.40 to 0.70. Green arrow indicates convergence point at iteration 8. Gray shading represents 95% confidence interval. (i) Eight-bit target ALS quantum signature (purple bars) representing binary encoding of biomarker pattern used for symbolic quantum search. (j) Quantum search results by Hamming distance. Green bars: perfect matches (distance=0, n=28), yellow: near matches (distance≤2, n=35), red: nonmatches (distance\u0026gt;2, n=26). Total samples = 89. (k) Optimization landscape heat map showing global optimum (white star) at threshold combination (0.6, 0.4). Color gradient from blue (low performance) to yellow (high performance, F1=0.967). (l) MAML adaptation trajectories over 8 steps for ALS patients (red) and controls (blue), demonstrating faster convergence despite higher initial loss in ALS. (m) Biomarker importance scores from meta-learning ranking. Bar heights represent normalized weights (0-1 scale): Beta Power (0.89), Motor Connectivity (0.84), Alpha Coherence (0.72), Temporal Variability (0.65), Gamma Phase (0.58). (n) Scalp topography highlighting biomarker localization with red/yellow indicating regions of maximum discriminative power, particularly over sensorimotor cortex. (o) Summary performance metrics box showing final classification results (F1=0.967, Precision=0.98, Recall=0.95) and key neurophysiological findings including 25% motor cortex beta reduction and 40% C3-C4 coupling loss. Abbreviations: ALS, amyotrophic lateral sclerosis; MAML, model-agnostic meta-learning; PLV, phase-locking value; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"Figure1ALSAnalysisPreview.png","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/5fefb14f80cdf33b3af80584.png"},{"id":103563011,"identity":"82a4e37a-106c-4330-947f-b3f1afc03311","added_by":"auto","created_at":"2026-02-27 06:27:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":618443,"visible":true,"origin":"","legend":"","description":"","filename":"CompleteALSEEGETManuscriptforSpringerNaturerev2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1_covered_7b3a57e9-7c38-4cd5-bcb8-8b2e6068dcfb.pdf"},{"id":94155487,"identity":"818f990f-1fa8-4ebb-b1d2-3c5b146ffe37","added_by":"auto","created_at":"2025-10-23 02:46:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27194,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1Electrodes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/cc14aad38f73751fbbe4b917.pdf"},{"id":94157083,"identity":"83562592-e114-4a2a-8c5c-6bdd60bdf096","added_by":"auto","created_at":"2025-10-23 03:02:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15796,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1SourceData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7513254/v1/cdd99ea8cdf43a8eae591ced.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Quantum-Inspired Framework with Meta-Learning for Enhanced ALS Biomarker Discovery and Brain-Computer Interface Applications","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Amyotrophic Lateral Sclerosis, EEG, quantum-inspired computing, meta-learning, braincomputer interface, biomarker discovery, adaptive filtering","lastPublishedDoi":"10.21203/rs.3.rs-7513254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7513254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Amyotrophic Lateral Sclerosis (ALS) presents significant challenges for biomarker identification and brain-computer interface (BCI) development due to progressive motor neuron degeneration and high signal-to-noise ratios in neurophysiological recordings. Traditional EEG analysis methods struggle with patient-specific variability and environmental artifacts, limiting their clinical utility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: We present an adaptive hybrid framework integrating dendritic-inspired noise filtering, quantum-inspired symbolic search with Hamming distance optimization, and meta-adaptive machine learning (MAML) for personalized adaptation. The system processes synchronized EEG and eye-tracking data from the EEGET-ALS dataset (45 ALS patients, 42 healthy controls), implementing multiple threshold optimization strategies including Bayesian optimization, evolutionary algorithms, and ensemble methods. The framework maintains a historical performance database enabling continuous learning and transfer learning capabilities across patient cohorts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Our framework achieved an F1-score of 0.967 (95% CI: 0.958-0.976), precision of 0.98, and recall of 0.95 in distinguishing ALS patients from healthy controls (Figure 1o). Key neurophysiological findings include ~25% beta power reduction (13-30 Hz) in central electrodes (Figure 1a-c), 40% motor connectivity loss particularly in C3-C4 coupling (Figure 1e-g), and preserved gamma oscillatory patterns (Figure 1d). The meta-learning module improved performance from baseline F1=0.667 to F1=0.751 within 1000 search iterations (Figure 1h), with transfer learning accelerating convergence by 5-10x (Figure 1l).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: The quantum-inspired adaptive framework establishes a new paradigm for ALS biomarker discovery and BCI development. By combining biologically-inspired filtering with continuous meta-learning, the system achieves clinical-grade accuracy while adapting to patient-specific variations. This approach enables robust communication interfaces for ALS patients and provides a generalizable framework for neurological biomarker discovery.\u003c/p\u003e","manuscriptTitle":"Adaptive Quantum-Inspired Framework with Meta-Learning for Enhanced ALS Biomarker Discovery and Brain-Computer Interface Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 02:46:33","doi":"10.21203/rs.3.rs-7513254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"668f389d-ec8c-4cdc-ae36-e50352e917cf","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56545803,"name":"Health sciences/Biomarkers"},{"id":56545804,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56545805,"name":"Health sciences/Neurology"},{"id":56545806,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-02-27T06:26:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 02:46:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7513254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7513254","identity":"rs-7513254","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}