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Beyond Traditional Neurofeedback
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From Neurofeedback To Bionics
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Neurotech Docs
An Introduction To Neurotechnology
Purpose of this primer
1. Brain-Computer Interface
Main ways to classify BCIs
By how signals are acquired
By how the user participates
By what the system is trying to do
Current state of the art
Contemporary examples
Where our team can contribute
2. Neuroimaging - how to read from the brain
Non-invasive
EEG
How it works
Strengths
Limitations
fNIRS / HEG
How it works
Strengths
Limitations
Minimally invasive + invasive
Sub-scalp EEG
Endovascular interfaces / Stentrode
ECoG
SEEG / depth electrodes
Intracortical arrays
3. Neurostimulation - how to write to the brain
Photic stimulation / photic driving
Auditory stimulation
Binaural beats and related auditory beat stimulation
tES: transcranial electrical stimulation
tDCS
tACS
Other tES variants
General strengths of tES
General limitations of tES
TMS
FUS: focused ultrasound
Infrared stimulation
Practical takeaway
4. Neurofeedback - feedback loops to train your brain
Core neurofeedback loop
EEG neurofeedback
Good uses of EEG neurofeedback
fNIRS neurofeedback
What neurofeedback is good for
Strengths
Weaknesses
Practical takeaway
5. Neuromodulation - hacking brain function and rhythms
Human performance applications
Psychological and clinical applications
Mood regulation
Anxiety and related conditions
Sleep
Practical takeaway
6. Final orientation for our team
Athletic Peformance Protocol
Part 1: The Wingate 5-Step Approach for Neurofeedback (NFB)
1. Introduction (Clinic Onboarding)
2. Identification (Finding the Optimal Protocol)
3. Simulation (Introducing Competitive Stress)
4. Transformation (Moving to the Practice Field)
5. Realization (Game-Day Execution)
Part 2: The "Neuro-Synergy" Protocol - Integrating Neuromodulation (NM) as a Primer for Neurofeedback (NFB)
1. Introduction (Baseline and Education)
2. Identification (Targeting and Calibration)
3. Simulation (The Synergistic Acquisition Phase)
4. Transformation (Field Integration)
5. Realization (Game-Day Execution and Pre-Performance Priming)
Operational Summary
Beyond Traditional Neurofeedback
Purpose
Core Shift
Innovation Direction 1: Build State Estimators, Not Protocol Silos
Calm Focus
Executive Readiness
Affective Regulation
Intentional Control
Innovation Direction 2: Make Neurofeedback Task-Coupled and Ecologically Valid
Innovation Direction 3: Build Multimodal and Confidence-Aware Feedback
Innovation Direction 4: Move Toward Decoded and Network-Level Neurofeedback
Innovation Direction 5: Introduce Social Neurofeedback and Hyperscanning
Innovation Direction 6: Treat Neurofeedback as an Experience-Design Problem
Recommended Lab Positioning
Practical R&D Priorities
Near-term
Mid-term
Long-term
One-Sentence Summary
Engineering Stack And Sdk
High-Level Stack Overview
Core Platform Engineering Stack
Signal Acquisition & Processing
Marker/Feature Extraction
Construct Axes Calculation
Task-Specific States
SDK for Student-Led Development
Defining New States
Feedback Policy & Interactive Design
Vibecoding in the SDK
From Neurofeedback To Bionics
Purpose
Strategic Framing
Core Thesis
Bridge 1: Neurofeedback as Training for Controllable Neural States
Bridge 2: Neurofeedback Sessions as Decoder-Training Data
Bridge 3: From Passive State Interface to Active Control Interface
Stage 1: Passive State Estimation
Stage 2: Closed-Loop Self-Regulation Training
Stage 3: Intentional State Modulation
Stage 4: Functional Interface Control
Stage 5: More Advanced BCI / Bionics Integration
Bridge 4: Construct Axes Are More Useful Than Single Markers
Bridge 5: Multimodal Systems Will Likely Matter More Than EEG Alone
Bridge 6: Closed-Loop Assistive Systems, Not Just Decoders
Bridge 7: How This Supports Future Bionics
What the Lab Should Build With This in Mind
Near-term
Mid-term
Long-term
Recommended Strategic Position
One-Sentence Summary
Neurofeedback Markers
Neurofeedback Protocol Matrix for Athletic Performance
Neurotechnology Protocol Matrix for Cognitive Enhancement & Wellbeing
Neurofeedback Protocol Matrix for Clinical Interventions (Wearable-Only)
Neuromodulation Targets
Neuromodulation and Stimulation Protocol Matrix for Athletic Performance
Stimulation Protocol Matrix for Cognitive Enhancement & Wellbeing
Neuromodulation / Stimulation Protocol Matrix for Clinical Interventions
Neurotech Platform Design
Core Principle
The Problem This Solves
The Pipeline
1. Marker / Feature Layer
2. Construct Axis Layer
3. Task-Specific State Layer
Athletic precision states
Cognitive enhancement / wellbeing states
Clinical states
Recommended Software Abstraction
Signals
Markers
Axes
States
Feedback Policy
Why This Matters
Marker-to-Axis-to-State Ontology
Canonical Axes
Internal Ontology Table
How to Use This in Practice
Rule 1: Do Not Build Apps Around Single Markers
Rule 2: Build Shared Axes First
Rule 3: States Are Domain-Specific
Example sport states
Example wellbeing states
Example clinical states
Worked Example: Athletic Performance
Goal State
Example Marker Contributions
Calm Focus
Motor Automaticity
Arousal Optimality
Fatigue / Instability
Example State Definition
Worked Example: From SMR to Calm Focus
Wrong approach
Better approach
Platform Design Recommendation
1. Marker Registry
2. Axis Estimators
3. State Decoders
4. Feedback Policy Engine
Summary
Markers
Construct Axes
Task-Specific States
Final Rule
Personalised State-Targeting Engine
Purpose
Core Principle
1. Define the Goal
If the client has no clear goal
2. Check Constraints
3. Run a Personalization Battery
Battery structure
Battery design note
What the battery should produce
4. Select the Target Axes
Recommended canonical axes
Default rule
5. Translate Axes Into Target States
Examples
6. Choose the Modality
Typical fit
7. Select Markers and Direction
Examples
Directionality rule
8. Generate the Prescription
Example
9. Use an Early Response Check
Possible outcomes
Personalization Decision Table
Design Rule
Summary
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