A multi-layer EEG analytics framework
From Signal Integrity to Research-Ready Neurophysiology
Neuromap™ combines structured data-quality controls, advanced computational neurophysiology, reference-based context, longitudinal analysis and specialist oversight in a single protocol-configured research workflow.
Technology architecture
- Data intake and provenance: Confirm file identity, study identifiers, format, channel information, acquisition metadata, visit information and approved transfer route.
- Signal and recording integrity: Check completeness, channel continuity, signal characteristics, artefact burden, usable duration and conformity with the study specification.
- Predefined analytical configuration: Select only the variables supported by the data and research question, with documented settings and version control.
- Quantitative feature generation: Produce approved spectral, temporal, regional, connectivity, complexity and network-derived outputs.
- Reference and longitudinal context: Apply eligible reference comparisons and within-participant or cohort-level change analysis.
- Quality review and interpretation: Review exceptions, limitations and agreed scientific interpretation before release.
- Structured delivery: Export reports, variables, quality flags, figures, data dictionaries and audit information.
Analytical domains
Signal and recording integrity
Technical checks assess whether available data are sufficiently complete and consistent for the agreed analysis.
Spectral and frequency analysis
Quantify configured absolute and relative activity, band relationships, peak-frequency characteristics and periodic or aperiodic components where supported.
Asymmetry and regional balance
Evaluate predefined regional and interhemispheric differences within the limits of the montage, reference and analysis model.
Connectivity and network analysis
Generate selected synchronisation, functional-connectivity, directed-connectivity and network-organisation measures where data quality and design permit.
Temporal dynamics and complexity
Analyse selected stability, variability, entropy, fractal, microstate or state-transition characteristics.
Cross-frequency relationships
Assess selected phase-amplitude, amplitude-amplitude or other coupling measures when sampling, duration and artefact control are adequate.
Longitudinal comparison
Use controlled settings to examine change between visits and across study milestones.
Reference and benchmark context
Apply approved reference comparisons only where the population, variable and dataset are suitable for the agreed research use.
Translational and exploratory outputs
Organise eligible EEG-derived variables into hypothesis-led research frameworks, without presenting exploratory measures as validated diagnostic or regulatory endpoints.
More than 170 computational outputs
The current Neuromap™ research framework describes more than 170 computational EEG and qEEG outputs across core signal, spectral, frequency, temporal, connectivity, complexity, network and translational domains. This number represents available analytical outputs, not 170 independently validated clinical biomarkers. The exact variables delivered in a project are selected prospectively and depend on acquisition quality, study design, reference suitability and contractual scope.
Reference and benchmark framework
Neuromap™ can apply age-, sex-, cohort- or study-relevant reference context where an eligible and governed comparison dataset is available. Every approved use identifies the reference source, population, sample characteristics, variable definition, transformation, limitations and permitted interpretation. A reference deviation does not independently establish diagnosis, causation, treatment effect or clinical significance.
AI-assisted analysis with human accountability
Neuromap™ uses computational and AI-assisted methods to organise complex outputs, detect predefined patterns and support consistent reporting. Automated outputs are not treated as self-validating. Quality rules, model or algorithm version, input suitability, uncertainty, exceptions and human-review level are defined for the project. Neuromap™ does not use website-described AI to make solely automated clinical decisions about individuals.
Reproducibility and traceability
- Version-controlled analysis configurations.
- Documented preprocessing and variable definitions.
- Recorded software, algorithm and reference-dataset versions.
- Explicit inclusion, exclusion and exception rules.
- Data dictionaries and output schemas.
- Traceable reruns and change-control records.
- Clear separation between exploratory and prespecified analyses.
Supported research data and interoperability
Compatibility is confirmed study by study. Common research workflows may include EDF, EDF+, BDF, BrainVision or other documented EEG formats, together with CSV or JSON metadata and output files. The acquisition guide defines channel naming, montage, reference, sample rate, duration, event markers, timepoint data and minimum clean-data expectations. Non-standard formats may require conversion and validation before analysis.
Data integrity and secure processing
The operational workflow is designed to support attributable, traceable and reviewable processing. Access is role-based, transfers use an approved secure route, participant identifiers are minimised, project configurations are controlled and deliverables are released through defined approval steps. Study-specific security, hosting, retention, audit and data-residency requirements are agreed before data transfer.
Regulatory boundary
This page describes research analytics capabilities. It does not state that every Neuromap™ function is a medical device, clinically validated, approved by the MHRA or approved for NHS use. Regulatory status depends on intended purpose and deployment. Where a medical-device or NHS deployment pathway applies, the relevant classification, conformity, clinical safety, DTAC, information-governance, cyber-security and procurement requirements must be completed separately.
Frequently asked questions
Common questions about the Neuromap™ analysis platform.
Does every project receive all 170-plus outputs?
No. Variables are selected prospectively according to the research question, acquisition quality and analysis plan.
Does a normative deviation mean disease?
No. A statistical deviation requires methodological and scientific context and does not independently establish diagnosis or clinical significance.
Can the same analysis be reproduced later?
The workflow is designed for traceability through controlled configurations, documented versions and output schemas. Reproducibility still depends on retained source data, software availability and the agreed environment.
Is Neuromap™ an autonomous AI diagnostic system?
No. The research website describes an AI-assisted analytical workflow with defined quality controls and human oversight, not autonomous clinical diagnosis.