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Neurotechnology Platform Design: From Markers to States

Core Principle

A neurotechnology platform should not treat individual neurofeedback markers as states.

Instead, it should use the following pipeline:

marker / feature -> construct axis -> task-specific state

This is the core abstraction that turns a collection of EEG / fNIRS / physiological features into a reusable software foundation for neurofeedback, cognitive training, and clinical applications.

The Problem This Solves

Literature tables give us many useful markers:

  • SMR
  • FMT
  • Theta/Beta Ratio
  • Upper Alpha
  • FAA
  • SCPs
  • Alpha/Theta
  • T3 Alpha / Temporal-Frontal Coherence
  • HbO Up-Regulation
  • Decoded EEG or fNIRS signals
  • etc.

But these are not directly the things an app should train.

For example:

  • SMR is not the same thing as calm focus
  • FAA is not the same thing as good mood
  • Theta/Beta Ratio is not the same thing as attention
  • FMT is not universally “up good” or “down good”

These markers are better treated as:

  • observations
  • control handles
  • evidence-backed proxies
  • inputs to latent construct estimation

So the platform should infer construct axes and then map those axes into task-specific states.

The Pipeline

1. Marker / Feature Layer

This is the raw library of things the platform can compute.

Examples:

  • EEG bandpower
  • individualized alpha
  • theta/beta ratio
  • SMR
  • FAA
  • coherence
  • SCP amplitude
  • HbO / HbR changes
  • decoded multivariate patterns
  • HRV
  • respiration
  • EDA
  • movement load
  • task performance metrics

This layer answers:

  • What can we measure?
  • From where?
  • With what latency?
  • Under what task assumptions?
  • For which populations or use cases?
  • With what evidence strength?

This should be implemented as a feature registry.

2. Construct Axis Layer

Construct axes are reusable latent dimensions that markers contribute to.

These axes are more stable and reusable than app-specific states.

A list of potential shared axes:

  • Arousal / activation
  • Task engagement
  • Cognitive control
  • Calm focus / stable attentional readiness
  • Motor automaticity
  • Perceptual breadth / visuospatial readiness
  • Affective regulation / emotional load
  • Executive recruitment / cognitive load
  • Fatigue / instability
  • Sleep readiness / sleep stability
  • Signal reliability

A single marker can contribute to multiple axes.

Example:

  • SMR may contribute to calm focus, motor stability, and sleep stability
  • FMT may contribute to task engagement and cognitive control
  • FAA may contribute to affective regulation
  • dlPFC HbO may contribute to executive recruitment and cognitive load

This is the key move: markers are grouped into latent constructs rather than treated as whole states

Similarly, each axis would be composed of multiple markers.

For example, calm focus could potentially be derived from SMR, upper alpha and high-beta.

3. Task-Specific State Layer

States are protocol-specific control abstractions built from combinations of axes.

Examples:

Athletic precision states

  • under-engaged
  • calm-focused
  • over-aroused
  • over-controlled
  • automatic / in-the-pocket
  • fatigued

Cognitive enhancement / wellbeing states

  • distracted
  • stable attention
  • mentally strained
  • inward / meditative
  • emotionally loaded
  • sleep-ready

Clinical states

  • hyperaroused
  • dysregulated
  • avoidant / shut down
  • regulated
  • impulsive / distractible
  • cognitively overloaded

States are what the app or protocol actually responds to.

The platform should be organized as:

signals -> markers -> axes -> states -> feedback policy

Signals

Raw EEG / fNIRS / physiology / behavior

Markers

Computed features and protocol-specific measurements

Axes

Shared latent constructs estimated from marker combinations

States

Task-specific regions or categories in axis space

Feedback Policy

What the app does next:

  • visual feedback
  • audio feedback
  • task adaptation
  • protocol progression
  • clinician recommendation
  • stimulation policy

Why This Matters

This architecture prevents the platform from becoming:

  • a bag of unrelated protocol scripts
  • a hardcoded “SMR app / FAA app / Theta-Beta app” zoo
  • a brittle collection of narrow decoders

Instead, it creates:

  • a shared ontology
  • reusable estimators
  • protocol portability
  • room for personalization
  • room for future learned representations or manifold models

Marker-to-Axis-to-State Ontology

Canonical Axes

Canonical Axis What It Represents Example App-Level States
Arousal / Activation How under-activated, optimal, or over-activated the person is under-engaged, optimal, over-aroused
Task Engagement Whether the person is actively on-task vs drifting or disengaging distracted, engaged, mind-wandering
Cognitive Control Degree of top-down task control and monitoring unfocused, controlled, over-controlled
Calm Focus / Stable Attentional Readiness Quiet, stable, low-noise task readiness scattered, calm-focused, sedated
Motor Automaticity Degree to which motor skill is flowing without verbal interference effortful, automatic, choking
Perceptual Breadth / Visuospatial Readiness Peripheral awareness, spatial monitoring, situational scanning tunnel vision, broad awareness, diffuse attention
Affective Regulation / Emotional Load Emotional distress, regulation success, approach vs avoidance distressed, regulating, settled
Executive Recruitment / Cognitive Load Working memory and prefrontal task recruitment under-recruited, optimal, overloaded
Fatigue / Instability Cognitive or performance drift, degradation, inconsistency fresh, stable, fatigued
Sleep Readiness / Sleep Stability Transition toward sleep and ability to maintain sleep-supportive physiology alert, winding down, sleep-ready, restless
Signal Reliability Whether the platform should trust the current signal estimate usable, noisy, invalid

Internal Ontology Table

Marker Domain Canonical Axis Typical Sign Context Sensitivity Confidence Candidate Protocol States
Sensorimotor Rhythm (SMR) Athletic, Wellbeing, Clinical Calm Focus; Motor Automaticity; Sleep Stability Usually positive for calm, stable, low-noise readiness Medium. Can reflect useful stability, but may also look high in low-demand or passive states Medium-High in sport; Medium in attention; Low-Medium in sleep calm-focused, stable attention, automatic, sleep-stable, under-engaged if high without behavioral engagement
Frontal Midline Theta (FMT) Athletic, Wellbeing Task Engagement; Cognitive Control Bidirectional. Can be beneficial when higher or lower depending on expertise and task High. Must be calibrated to task and person Medium engaged, focused, over-controlled, flow-ready, cognitively strained
Theta/Beta Ratio Athletic, Wellbeing, Clinical Task Engagement; Distractibility / Attention Regulation Lower ratio often better for focused alertness Medium. Stronger in ADHD-like settings than general wellness Medium distractible, engaged, impulsive, task-ready
Multi-Band Reaction Speed Profile
(SMR and Beta1 up / Theta and Beta2 down)		 Athletic Calm Focus; Task Engagement; Fatigue / Instability Positive when stable focused alertness increases and drift decreases Medium Medium locked in, scattered, mentally cooked, reaction-ready
Upper Alpha / Individualized Alpha Wellbeing, Athletic, Clinical Calm Focus; Memory Readiness; Perceptual Stability Usually positive for calm attentional control and working-memory support Medium. Better when individualized around IAF Medium-High stable attention, deep work, broad readiness, calm cognitive control
Alpha Band Up-Training / CVSA Athletic Perceptual Breadth / Visuospatial Readiness Positive for broad peripheral awareness and covert visuospatial attention Medium-High. Likely task-specific and best with behavioral anchors Emerging tunnel vision, broad field awareness, scanning-ready
COSMI Index Athletic Calm Focus; Task Engagement; Motor Readiness Positive when SMR rises and distracting/noisy bands drop Medium Emerging reaction-ready, precise, stable, mentally noisy
ACC Modulation / Arousal Regulation Athletic Arousal / Activation Positive when arousal moves toward an optimal band, not simply lower High. The target is optimality, not one direction Emerging flat, optimal, overheated, panic-prone
Left Temporal Alpha (T3) Athletic Motor Automaticity Usually positive for reducing verbal-analytic interference in precision skills High. Most relevant in expert self-paced precision contexts Medium automatic, overthinking, choking, fluent execution
Temporal-Frontal Coherence Athletic Motor Automaticity; Cognitive Control Often interpreted relative to reduced conscious overcontrol High Medium automatic, effortful control, paralysis by analysis
FAA (Frontal Alpha Asymmetry) Wellbeing, Clinical Affective Regulation / Emotional Load Protocol-dependent; often tied to healthier approach-oriented affect Medium-High. Evidence is mixed and targetability is not perfectly settled Medium distressed, regulating, settled, pre-sleep emotionally downshifted
Decoded EEG Emotion-State / Reappraisal Signal Wellbeing, Clinical Affective Regulation; Cognitive Reappraisal Positive when decoded affect-regulation pattern matches desired state High. Depends on decoder training and calibration Emerging emotionally loaded, reappraising, resilient, settled
Alpha/Theta Ratio / Alpha-Theta Training Wellbeing, Clinical Arousal / Activation; Affective Regulation; Sleep Readiness Often positive for downshifting, inwardness, relaxation Medium Medium-Low to Medium relaxed inward, unwinding, meditative, sleep-ready
SCPs (Slow Cortical Potentials) Wellbeing, Clinical, Accessibility / BCI Task Engagement; Intentional Control Depends on whether the protocol trains activation or deactivation Medium Medium-High clinically engaged, release, intentional control, accessibility control state
PCC / DMN Downregulation Wellbeing Affective Regulation; Inward Attention; Mind-Wandering Control Positive when self-referential drift decreases during meditation-like states High. Measurement often indirect unless imaging is used Emerging mind-wandering, present, inward, meditative
dlPFC HbO Up-Regulation Athletic, Wellbeing, Clinical Executive Recruitment / Cognitive Load; Task Engagement Positive up to a point. More is not always better if overload appears Medium Medium / Emerging under-recruited, optimal executive engagement, overloaded
Decoded Prefrontal fNIRS Patterns Wellbeing Executive Recruitment; Cognitive Control Positive when decoded control-related patterns strengthen High. Heavily decoder-dependent Emerging anti-distraction, executive-ready, controlled, resilient
Network-Based fNIRS Small-Worldness Wellbeing Executive Recruitment; Cognitive Control; Fatigue / Instability Positive when network efficiency aligns with lower cognitive load and stronger control High Emerging efficient-control, overloaded, unstable-control
SMR-Linked Sleep Stability / Spindle-Adjacent Training Wellbeing, Clinical Sleep Readiness / Sleep Stability Potentially positive for sleep-supportive stability, but evidence is mixed Medium Mixed winding down, sleep-stable, restless
Autism-Oriented EEG Self-Regulation Targets
(SCP, beta/theta, mu / alpha etc.)		 Clinical Task Engagement; Affective Regulation; Executive Recruitment Protocol-dependent High. Highly population- and target-specific Emerging-Medium engaged, dysregulated, more regulated, overloaded
ADHD-Oriented EEG Targets
(Theta/Beta, SMR, SCPs)		 Clinical Task Engagement; Cognitive Control; Fatigue / Instability Protocol-dependent; usually lower distractibility and higher control are desirable Medium Mixed / Low distractible, task-engaged, impulsive, fatigued
Tinnitus Alpha/Delta Targets Clinical Affective Regulation; Sensory Distress / Symptom Load Protocol-dependent and likely indirect High Emerging / Mixed distressed, symptom-loaded, more regulated
MCI Cognitive EEG Targets
(alpha, beta, SMR/theta combinations)		 Clinical Executive Recruitment; Memory Readiness; Task Engagement Usually positive when supporting cognitive engagement and memory stability Medium Emerging-Medium under-recruited, cognitively engaged, fatigued

How to Use This in Practice

Rule 1: Do Not Build Apps Around Single Markers

Do not build:

  • an SMR app
  • an FAA app
  • a theta/beta app

Build:

  • a calm-focus estimator
  • an overcontrol detector
  • a sleep-readiness estimator
  • an affective regulation estimator
  • a competition-readiness estimator

Markers should sit underneath the estimator.

Rule 2: Build Shared Axes First

A platform should estimate reusable axes before it estimates task states.

Recommended shared axes for version 1:

  • Arousal / Activation
  • Task Engagement
  • Cognitive Control
  • Calm Focus
  • Motor Automaticity
  • Affective Regulation
  • Executive Recruitment
  • Fatigue / Instability
  • Sleep Readiness
  • Signal Reliability

These axes can then be reused across:

  • sport
  • wellbeing
  • cognitive enhancement
  • clinical training

Rule 3: States Are Domain-Specific

Example sport states

  • under-engaged
  • calm-focused
  • over-aroused
  • over-controlled
  • automatic
  • fatigued

Example wellbeing states

  • distracted
  • stable attention
  • mentally strained
  • inward / meditative
  • emotionally loaded
  • sleep-ready

Example clinical states

  • hyperaroused
  • dysregulated
  • avoidant / shut down
  • regulated
  • impulsive / distractible
  • cognitively overloaded

Worked Example: Athletic Performance

Goal State

Calm-focused and competition-ready

This should not be inferred from SMR alone.

It should be estimated from multiple axes, for example:

  • Calm Focus
  • Motor Automaticity
  • Arousal Optimality
  • Fatigue / Instability

Example Marker Contributions

Calm Focus

  • SMR
  • Upper Alpha
  • Theta/Beta Ratio
  • COSMI components

Motor Automaticity

  • T3 Alpha
  • Temporal-Frontal Coherence
  • Expert-specific FMT effects

Arousal Optimality

  • ACC modulation
  • stress-sensitive beta features
  • optional HRV / respiration if available

Fatigue / Instability

  • performance drift
  • RT variability
  • dlPFC HbO load
  • multi-band degradation over time

Example State Definition

A rough internal rule might be:

competition-ready when:

  • Calm Focus is high
  • Automaticity is high
  • Arousal is within an optimal band
  • Fatigue is low
  • Signal quality is acceptable

This means:

SMR does not directly become “competition-ready”
It becomes one weighted contributor to the Calm Focus axis, which then contributes to the final competition-ready state.

Worked Example: From SMR to Calm Focus

Wrong approach

SMR = calm focus

Better approach

SMR contributes to a Calm Focus construct together with other markers.

Example conceptual composite:

Calm Focus is estimated from:

  • SMR
  • Upper Alpha
  • High-Beta or tension marker
  • Theta/Beta Ratio
  • optional EMG / motion noise
  • optional behavioral stability

Then the athlete’s state is determined from that axis plus others.

This gives:

SMR -> Calm Focus axis -> Competition-Ready state

But in practice the real flow is:

SMR + other markers -> Calm Focus axis
then
Calm Focus + other axes -> Competition-Ready state

Platform Design Recommendation

The platform should implement:

1. Marker Registry

Stores:

  • what the marker is
  • how it is computed
  • where it is measured
  • required preprocessing
  • domain relevance
  • evidence confidence
  • known caveats

2. Axis Estimators

Reusable models that combine markers into construct axes.

Examples:

  • Calm Focus Estimator
  • Arousal Estimator
  • Affective Regulation Estimator
  • Executive Recruitment Estimator

3. State Decoders

Protocol-specific mappings from axes to app states.

Examples:

  • Sport readiness decoder
  • Sleep readiness decoder
  • Mood regulation decoder
  • ADHD attention-state decoder
  • Trauma regulation decoder

4. Feedback Policy Engine

Decides what the app does once state is inferred.

Examples:

  • increase / decrease feedback intensity
  • adapt task difficulty
  • hold state
  • encourage down-regulation
  • reinforce best-state similarity
  • trigger clinician note or safety pause

Summary

Markers

Evidence-backed measurements and control handles

Construct Axes

Reusable latent dimensions that markers contribute to

Task-Specific States

Protocol-level control abstractions built from axes

Final Rule

Markers are not states.
Axes are reusable constructs.
States are protocol-specific regions in axis space.

So the correct abstraction is:

signals -> markers -> axes -> states -> feedback policy

That is the foundation for a neurotechnology platform that supports:

  • athletic performance products
  • cognitive enhancement products
  • wellbeing products
  • clinical neurofeedback products
  • future manifold / learned-representation layers