EEG and temporal cognition
Research on human time reproduction, temporal bias, behavioural history, and the incremental information carried by trial-level neural state.
Computational neuroscience · research systems · neurotechnology
Peiman Jannatipour — Computational Neuroscience Research
EEG · Human Timing · Bayesian Modelling · Developmental Neuroimaging · Scientific Software · Neurotechnology
I work across computational neuroscience, EEG, temporal cognition, Bayesian modelling, developmental neuroimaging, scientific software, and privacy-aware neurotechnology. My research asks how behavioural context, recent experience, and neural state shape human cognition, while my systems work focuses on making complex scientific workflows more reproducible, interpretable, and usable.
The portfolio brings together empirical neuroscience, computational modelling, ongoing ABCD developmental-neuroimaging work, NeuroLab OS, the NDMS invention programme, and evidence-review systems such as SMIS-ODS. Public pages distinguish preprints, manuscripts, research lines, prototypes, and patent-pending work so that status and evidence remain clear.
Research profile
Connected computational neuroscience research lines
Bayesian and computational modelling
Interpretable models of human behaviour with explicit diagnostics for central tendency, variability structure, prediction, and model misspecification.
Developmental neuroimaging
Ongoing ABCD research on adolescent brain maturation, resting-state connectivity, and dimensional neurodevelopmental traits, presented publicly at a non-spoiling research-line level.
Research software and invention
Local-first scientific software, privacy-aware multimodal neurotechnology, and structured evidence systems designed around traceability and responsible use.
Scientific approach
A computational neuroscience programme built around evidence, models, and reproducibility
- Reproducibility and provenance
- Explicit quality control
- Model falsification and diagnostics
- Privacy-aware system design
- Clear separation of evidence, inference, and product claims
My empirical neuroscience work focuses on timing and cognitive variability: what people reproduce, how context and previous trials bias that behaviour, and whether neural state adds information beyond those behavioural structures.
My computational work treats model fit as only one part of the scientific question. I emphasize predictive evaluation, residual diagnostics, model limitations, and interpretable mechanisms rather than relying on headline accuracy alone.
Alongside published and preprint work, I maintain active research lines in developmental neuroimaging and research infrastructure. High-level descriptions are public, while unpublished hypotheses, analyses, and results remain private until they are ready for scientific release.
Research areas
Computational neuroscience, EEG, human timing, and neurotechnology
My work is organized around connected research lines rather than a single method. The common thread is interpretable modelling, careful validation, reproducible analysis, developmental neuroimaging, and systems that keep scientific assumptions visible to the researcher.
EEG and neural time-series
Analysis of sensor-level neural signals, residual prediction, and brain-behaviour relationships in human timing and cognitive variability.
Why it matters: tests whether neural signals add useful information beyond what behaviour already explains.
EEG research details →Temporal cognition and human time reproduction
Research on systematic bias in time reproduction, contextual effects, recent-trial influences, and computational accounts of temporal estimation.
Why it matters: explains why reproduced durations systematically drift with context and recent experience.
Human timing research →Bayesian and state-space modelling
Interpretable behavioural models emphasizing central tendency, multiplicative variability, predictive diagnostics, and explicit model misspecification.
Why it matters: makes model successes and failure modes visible instead of relying on fit alone.
Bayesian model details →ABCD developmental neuroimaging
An ongoing research line using ABCD data to study adolescent brain maturation, resting-state connectivity, and dimensional autism/ADHD trait co-occurrence without disclosing unpublished analyses.
Why it matters: studies developmental variation at a scale that can connect brain maturation with dimensional traits.
ABCD research scope →Neurotechnology and scientific systems
Design of privacy-aware and reproducible systems for neural analysis, modelling, simulation, multimodal inference, and research translation.
Why it matters: turns complex scientific workflows into systems that are easier to inspect, reproduce, and audit.
Scientific software systems →Evidence systems and scientific decision support
Structured evidence-review work focused on separating claims, sources, study design, outcome relevance, risk of bias, and support direction.
Why it matters: separates evidence quality from the claim being made before a conclusion is presented.
Evidence-system details →In progress
Current research and systems work
Ongoing work is shown at the level appropriate for a public research portfolio. Unpublished hypotheses, results, and implementation details are intentionally not disclosed.
ABCD Autism-ADHD Developmental Neuroimaging Research Line
Research lineABCD-focused developmental neuroimaging work on adolescent brain maturation, resting-state connectivity, and dimensional autism/ADHD trait co-occurrence.
ABCDDevelopmental neuroimagingResting-state fMRI
ABCD project overview →NeuroLab OS
Technical beta / research softwareLocal-first research workbench for neural and biomedical time-series ingestion, explicit QC, model fitting, simulation, perturbation, provenance, and reporting.
ElectronFastAPINeural time-seriesReproducibility
NeuroLab OS overview →Masking Index Prototype
NDMS · patent pending / technology developmentNDMS privacy-aware multimodal neurotechnology programme tied to the PCT application, with emphasis on quality-aware multimodal inference, edge-oriented processing, and responsible-use boundaries.
NDMSPrivacyMultimodal AIEdge inferencePCT application filed
Masking prototype overview →SMIS-ODS
Research-use conceptStructured evidence-review system that maps supplement claims to sources, study designs, outcome relevance, source reliability, support direction, and risk of bias.
Evidence systemsStructured reviewRisk of biasMisinformation screening
SMIS-ODS overview →Research outputs
Publications, preprints, and manuscripts
Research Square preprint · 2026
Sensor-Level EEG Residual Prediction of Temporal Reproduction Bias
A Research Square preprint examining whether trial-level EEG information explains residual variation in time reproduction beyond behavioural structure already captured by duration, context, and recent experience.
View preprint →SSRN preprint · 2026
A Bayesian Log-Time State-Space Clock for Human Time Reproduction
An SSRN preprint presenting a Bayesian log-time state-space account of human time reproduction with explicit attention to central tendency, multiplicative variability, prediction, and diagnostic model adequacy.
View preprint →Technical manuscript · 2026
NeuroLab OS: A local-first software environment for scientist-reviewed neural time-series modelling and provenance-aware simulation
A technical manuscript describing a local-first scientific workbench for neural time-series ingestion, explicit quality control, computational model fitting, in silico perturbation workflows, and provenance-aware reporting. A complete journal-style full-text article view is available on this site.
Read full article →SSRN preprint · 2025
A Graph-Theoretic Framework for Representing Spacetime as Temporal Layers of Spatial Graphs
An exploratory theoretical preprint representing spacetime as ordered layers of spatial graphs, with emphasis on discrete structure, temporal layering, invariance, and graph-based modelling.
View SSRN preprint →Evidence and status
External sources and clear research-status definitions
Public claims are linked to scholarly identities, DOI records, project sources, or explicit status pages when an appropriate public source exists. Research and technology labels are kept distinct so that a preprint, manuscript, technical beta, or patent-pending invention is not silently upgraded into a stronger claim.
External verification
ORCID identity ↗
Persistent scholarly identity record
ResearchGate profile ↗
Public scholarly profile and research presence
GitHub projects ↗
Public code and project identity
DOI-linked research →
Preprints and manuscripts with source links
PCT status context →
Public invention and patent-status explanation
Status guide
- Preprint
- A publicly available research manuscript; it should not be described as peer reviewed unless a verified journal version is linked.
- Manuscript
- Research work presented with its current public status; unpublished analyses and results remain outside the public description.
- Technical beta
- Software verification and release-level testing do not imply biological, diagnostic, therapeutic, or clinical validation.
- Patent pending
- An application or PCT-stage invention status is not the same as an issued patent or a validated commercial product.
About
Researcher, inventor, and scientific-systems builder
Peiman Jannatipour works at the intersection of computational neuroscience, neural time-series analysis, behavioural modelling, developmental neuroimaging, scientific software, and neurotechnology. Across projects, the emphasis is on technically explicit methods, conservative interpretation, reproducible workflows, and communication that makes the evidence boundary visible. The portfolio includes public preprints, manuscripts in development, research software, an international patent application, and exploratory research systems.