Reproducible EEG
Research Infrastructure.
ARCHON Ψ is an integrated edge-to-audit pipeline for producing traceable, reproducible records from real-time EEG research workflows. Low-latency processing. Physics-informed benchmarks. Calibrated scoring. Tamper-evident provenance.
An Integrated, Reproducibility-First
Research Pipeline.
Current EEG software commonly treats acquisition, preprocessing, classification, evaluation, and documentation as separate steps. This fragmentation makes it difficult to reproduce results, detect post-capture alteration, or distinguish raw observations from model inference.
The Fragmentation Problem
Existing systems may provide dashboards, model predictions, or data storage, but they generally do not integrate low-latency edge processing, participant-held-out benchmarking, explicit uncertainty, and evidence-bound provenance into one research workflow.
Edge-Native Artifact-Aware Processing
Test an optimized real-time artifact-removal and preprocessing path on ARM Cortex-M-class hardware. The research goal is to characterize the latency, artifact-suppression, and signal-preservation tradeoff under a specified protocol — not to claim therapeutic efficacy.
Physics-Informed Classification Benchmark
Test whether physiologically motivated constraints improve robustness, calibration, or cross-session/participant generalization relative to matched conventional baselines. Physics-informed modeling is treated as a testable hypothesis, not an assumed advantage.
Candidate Task-Linked Composite Index
Evaluate a preregistered composite of defined signal features and task context for incremental predictive value over simpler features. Not presented as a consciousness measure, clinical biomarker, intelligence score, or personal evaluation.
Evidence-Bound Provenance
Bind dataset references, annotation-rubric versions, preprocessing configuration, model version, evaluation outputs, reviewer actions, and corrections to a tamper-evident record. Verifies traceability and computational lineage — does not prove that an inferred mental state is true or clinically meaningful.
Technical Novelty
The value lies in making bounded EEG workflow claims inspectable, replayable, correctable, and comparable across controlled research conditions.
Four Core Technical Risks.
Phase I will test whether a provenance-instrumented, edge-deployed EEG pipeline can produce reproducible, calibrated classifications of protocol-defined, non-clinical task labels across held-out participants and sessions.
Phase I Hypothesis
“A provenance-instrumented, edge-deployed EEG pipeline can produce reproducible, calibrated classifications of protocol-defined, non-clinical task labels across held-out participants and sessions, while maintaining auditable lineage from acquisition through evaluation.”
Edge Latency & Signal Integrity
Latency may be achieved only by degrading relevant signal content.
Compare artifact removal against a prespecified benchmark using signal-distortion measures and retain raw/reprocessed references.
Generalization & Calibration
Performance may collapse when users, sessions, devices, or time periods are held out.
Lock splits and preprocessing before testing; compare participant-, session-, and device-held-out results where feasible.
PINN Value Relative to Baselines
Physiologically motivated constraints may not improve performance or may reduce stability under multiscale signal conditions.
Use fixed compute budgets, multi-seed trials, adaptive sampling/domain decomposition experiments where justified.
Reproducible Provenance
Integrity controls may detect post-capture changes but fail to capture upstream sensor, labeling, replay, or version-mismatch errors.
Evaluate against a documented attack suite; report detection rate and false-positive/false-negative rates.
Go/No-Go Advancement Criteria
Accountable Cognitive-Data
Infrastructure.
Research Institutions
Universities and labs conducting controlled EEG, HCI, training, or signal-analysis studies.
Cognitive-Performance Labs
Teams needing practical tools for low-latency acquisition, protocol consistency, and model evaluation.
Enterprise R&D Teams
Organizations requiring version control, reproducible reporting, and auditable evidence trails.
Current Workflow Fragmentation
Current workflows are fragmented across acquisition software, preprocessing scripts, model notebooks, annotation files, evaluation reports, and compliance documentation. This fragmentation makes it costly to reproduce results, audit errors, compare versions, or preserve an evidence trail for internal review, collaborators, and funders.
ARCHON Ψ Research-Grade Workflow Platform
Research-Integrity Boundary
The platform does not sell consciousness measurement, diagnosis, treatment, employee surveillance, cognitive scoring, or transferable claims on human cognition.
Scale Path
From research teams to regulated and high-accountability R&D settings that require stronger auditability of AI-assisted research — including clinical-research support workflows, institutional-review documentation, and quality-system evidence management.
Waveform Tech LLC
Developing ARCHON Ψ as a reproducibility and accountability platform for real-time cognitive-data research workflows.
Principal Investigator & Technical Founder
The project will be led by the company's technical founder and principal investigator, who will direct system architecture, edge implementation, model evaluation, provenance design, and commercialization discovery.
Phase I work will be organized around reproducible technical milestones: locked protocols, held-out evaluation, matched baselines, stress testing, and a versioned technical dossier.
Domain Expertise — Advisors & Contractors
Phase I Objective
Establish technical feasibility for an edge-to-audit EEG pipeline before pursuing any clinical, therapeutic, or high-impact application. All technical claims will remain bounded to the evidence generated in Phase I.
Request a Technical Briefing.
Interested in ARCHON Ψ for your research lab, institution, or R&D team? We evaluate alignment before scheduling.