# 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.

---

## Recommended Software Abstraction

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**<br>(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**<br>(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**<br>(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**<br>(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