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

Instead, it should use the following pipeline:

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.

---

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:

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

---

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**.

---

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:

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:

Similarly, each axis would be composed of multiple markers.

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

---

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

Examples:

- under-engaged

- calm-focused

- over-aroused

- over-controlled

- automatic / in-the-pocket

- fatigued

- distracted

- stable attention

- mentally strained

- inward / meditative

- emotionally loaded

- sleep-ready

- 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:

Raw EEG / fNIRS / physiology / behavior

Computed features and protocol-specific measurements

Shared latent constructs estimated from marker combinations

Task-specific regions or categories in axis space

What the app does next:

- visual feedback

- audio feedback

- task adaptation

- protocol progression

- clinician recommendation

- stimulation policy

---

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

---

| 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 |

---

| 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 |

---

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.

---

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

---

- under-engaged

- calm-focused

- over-aroused

- over-controlled

- automatic

- fatigued

- distracted

- stable attention

- mentally strained

- inward / meditative

- emotionally loaded

- sleep-ready

- hyperaroused

- dysregulated

- avoidant / shut down

- regulated

- impulsive / distractible

- cognitively overloaded

---

This should not be inferred from SMR alone.

It should be estimated from multiple axes, for example:

- SMR

- Upper Alpha

- Theta/Beta Ratio

- COSMI components

- T3 Alpha

- Temporal-Frontal Coherence

- Expert-specific FMT effects

- ACC modulation

- stress-sensitive beta features

- optional HRV / respiration if available

- performance drift

- RT variability

- dlPFC HbO load

- multi-band degradation over time

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.

---

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:

But in practice the real flow is:

**SMR + other markers -> Calm Focus axis**

then

---

The platform should implement:

Stores:

- what the marker is

- how it is computed

- where it is measured

- required preprocessing

- domain relevance

- evidence confidence

- known caveats

Reusable models that combine markers into construct axes.

Examples:

- Calm Focus Estimator

- Arousal Estimator

- Affective Regulation Estimator

- Executive Recruitment Estimator

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

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

---

Evidence-backed measurements and control handles

Reusable latent dimensions that markers contribute to

Protocol-level control abstractions built from axes

**Markers are not states.**

**Axes are reusable constructs.**

So the correct abstraction is:

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