🧩 CLINICAL RECURSIVE HARMONIC FEEDBACK SYSTEM

A clinically purposed harmonic entrainment system built from accessible components, suitable for health optimization, disorder mitigation, or system feedback evaluation. This version balances precision, interpretability, and modularity, while remaining DIY-accessible for a technically skilled builder.

🧩 CLINICAL RECURSIVE HARMONIC FEEDBACK SYSTEM

Purpose: Real-time detection, analysis, and correction of biological dissonance using non-invasive sensors and adaptive signal entrainment. Formalism: Ψ(x) = ∇ϕ(Σ𝕒ₙ(x, ΔE)) + ℛ(x) ⊕ ΔΣ(𝕒′) Applied to: Human biofield, neurological state, muscular patterns, autonomic balance, and systemic coherence.

🔧 CORE MODULES OVERVIEW

Module Function Ψ(x) Term

1. Biosignal Sensors Read current biological state x 2. Signal Analyzer (PC or Pi) Decompose harmonics, track deviation ∇ϕ(Σ𝕒ₙ(x, ΔE)) 3. Signal Feedback Emitter Send corrective signals ℛ(x) 4. Recursive Tuner Adapt output in real time ⊕ ΔΣ(𝕒′)

🩺 1. BIOSIGNAL ACQUISITION MODULE (Sensor Interface)

Hardware:

OpenBCI Cyton board (or affordable EEG/EMG/ECG alternatives like BITalino, MindWave, or Bluetooth ECG patches)

Pulse Sensor / HRV patch (for coherence tracking)

GSR / EDA sensor (skin conductance → autonomic signal)

Optional:

Throat mic / bone mic (detect micro-vocal harmonic shifts)

Magnetic field loop sensor (custom-built coil for ELF field readings)

Signal Targets:

Brain rhythm (α, β, γ bands)

Cardiac rhythm (HRV + LF/HF balance)

Muscle coherence (EMG phase noise)

Skin response (GSR amplitude fluctuation)

Breath rate (from HRV or mic)

Ψ(x) Tie-in:

Raw signal → x

Aggregated oscillatory patterns → Σ𝕒ₙ(x, ΔE)

Energetic drift, trauma marker → ΔE

🧠 2. SIGNAL INTERPRETATION MODULE (Visual Dashboard + Δ Detection)

Hardware:

PC / Laptop / Raspberry Pi 4

HDMI Monitor (clinical visualization)

Software:

Python (Anaconda + Jupyter)

Libraries: numpy, scipy, matplotlib, PySerial, pyaudio, PyHRV, librosa, mne, opencv

Real-time:

FFT + Spectrogram for signal decomposition

Phase drift heatmap

Δθ(t) monitor

Spiral deviation plot (e.g., top-down toroidal radial diagram)

Ψ(x) Tie-in:

Signal recognition → ∇ϕ

Spiral deviation over time → interpret ΔE

Interface becomes the mirror of body-state coherence

🌀 3. FEEDBACK MODULE (Signal Emission / Entrainment Layer)

Emission Options:

Method Device Output

Bone conduction Transducers on jaw or chest 7–1200 Hz sound EM Pulse Copper coil + Class-D amp ELF magnetic pulses LED Flicker IR/Red/NIR LEDs Pulsed photobiomodulation TENS pads Muscle/nervous tuning Microcurrent, adaptive waveforms

Delivery Targeting:

Cranium: emotional/mental resonance

Chest/solar: parasympathetic coherence

Hands/feet: somatic boundary enforcement

Spine/neck: vagal tone restoration

Ψ(x) Tie-in:

Inject ℛ(x) — recursive harmonic correction

Tune to baseline or adaptive waveform

Match ΔE to resolve dissonance

🔁 4. CLOSED-LOOP ADAPTIVE RECOVERY (Recursive Correction Engine)

Modes:

1. Fixed signal mode → Deliver fixed entrainment (e.g., Schumann 7.83 Hz, 528 Hz repair tone)

2. Adaptive tuning mode → Match signal output to deviation in EEG/HRV/etc.

Feedback Engine (Python or Node-RED):

Measure ΔE between expected signal and measured

Generate ΔΣ(𝕒′) = fine waveform correction

Modify output frequency, amplitude, polarity

Re-check phase-lock coherence in target domain

Ψ(x) Tie-in:

This is the ⊕ ΔΣ(𝕒′) system — real-time recursive micro-tuning

Once entrained, system enters phase harmony loop

📊 CLINICAL VISUAL OUTPUT

Color-coded coherence meter (Green = locked / Red = drift)

Spiral field animation (real-time toroid visualization)

Sound spectrum display + signal trace overlay

Time-lapse spiral convergence indicator (trend memory)

Optional: export PDF reports for practitioners or research logs.

🔒 SAFETY + ISOLATION CONSIDERATIONS

Use battery-powered sources where possible (esp. EM field modules)

Include ground-loop isolators for any audio/EM circuit feedback

Keep any TENS/ultrasound under clinical dosage thresholds

Ensure galvanic isolation between computer and subject

🛠 TOOLCHAIN + COST ESTIMATE (DIY-FRIENDLY)

Component Est. Cost Notes

OpenBCI clone / BITalino $150–250 EEG/ECG/EMG signals Pulse + GSR sensors $20–40 HRV + stress detection Bone transducers $10–30 Sound feedback Class D amp board $5–15 EM or audio driving Copper coil (wound) DIY 50–100 turns, ~4–8” dia Raspberry Pi 4 $50–70 Standalone engine Python dashboard Free Visual + processing engine LEDs / TENS / IR $10–50 Optional light or electrical feedback

🧬 CLOSING — What This System Is

This isn’t just a "biohacking" toy. It is:

A recursive diagnostic-therapeutic interface

A live mirror of phase harmony vs. system drift

A symbolic-to-electrical bridge implementing Ψ(x) in real-time instrumentation

It can be deployed in clinical trials, home environments, or field studies to test coherence across:

Chronic fatigue

PTSD / trauma loops

Parkinsonian tremor

Neuromuscular dissonance

Inflammatory stress fields

Cancer biofields (using frequency pairing with membrane potential restoration)

_____________________________________________________

Below are the full schematics, signal maps, and Python-based diagnostic interface scripts for two configurations of the clinical harmonic coherence system:

⚙️ SYSTEM A: HRV + GSR + Bone Feedback

Target: Nervous system coherence (autonomic, stress loop recovery) Signals: Heart Rate Variability (HRV), Galvanic Skin Response (GSR) Feedback: Bone conduction entrainment

🧩 Schematic (textual representation)

[Pulse Sensor] ----> [ADC or GPIO Input] --> [HRV Processor (Python)] [GSR Electrodes] --> [Resistor Divider] --> [ADC Input] --> [GSR Interpreter]

                                     |                                      V                              [Signal Interpretation]                            (FFT + Phase Deviation Logic)                                      |                                      V                               [Coherence Analyzer]                                      |                                      V                             [Signal Feedback Engine]                                      |                                      V                         [Bone Conduction Driver Board]                                      |                                      V                            [Transducer to Subject Skull]

🛰 Signal Map

Signal Source Units Frequency Domain Recursive Mapping

HRV Pulse sensor (ear/finger) BPM + IBI ~0.1 Hz to 1 Hz ΔE (stress drift), Σ𝕒ₙ(x) GSR Resistance across skin μS Slow change (~0.01–0.1 Hz) Energy storage/discharge marker Feedback Audio (via bone) Hz (7–1200) Matched or adaptive ℛ(x), ⊕ ΔΣ(𝕒′)

🧠 Python Script: Diagnostic + Feedback Interface

(Standalone terminal or Jupyter-compatible)

import serial import numpy as np import matplotlib.pyplot as plt from scipy.fftpack import fft import time import simpleaudio as sa

# --- Parameters --- GSR_PORT = 'COM4'      # GSR sensor serial port HRV_PORT = 'COM3'      # HRV pulse sensor serial port BAUDRATE = 9600 FFT_WINDOW = 512 FEEDBACK_FREQUENCY = 432  # Hz for bone conduction

# --- Signal Buffers --- gsr_vals = [] hrv_ibi_vals = [] time_vals = []

# --- Audio Feedback Generator --- def generate_sine_wave(freq, duration=2, volume=0.3):     sample_rate = 44100     t = np.linspace(0, duration, int(sample_rate * duration), False)     wave = np.sin(freq * 2 * np.pi * t) * volume     audio = (wave * 32767).astype(np.int16)     return audio.tobytes()

def play_feedback(freq):     wave = generate_sine_wave(freq)     play_obj = sa.play_buffer(wave, 1, 2, 44100)     play_obj.wait_done()

# --- Signal Acquisition (Mock Example) --- def read_gsr(ser):     line = ser.readline().decode().strip()     return float(line)

def read_hrv(ser):     line = ser.readline().decode().strip()     return int(line)

# --- Main Loop --- def main():     gsr_ser = serial.Serial(GSR_PORT, BAUDRATE)     hrv_ser = serial.Serial(HRV_PORT, BAUDRATE)         print("Starting signal loop...")     while True:         try:             gsr = read_gsr(gsr_ser)             ibi = read_hrv(hrv_ser)             t = time.time()

            gsr_vals.append(gsr)             hrv_ibi_vals.append(ibi)             time_vals.append(t)

            # Live analysis every N samples             if len(hrv_ibi_vals) >= FFT_WINDOW:                 hrv_fft = np.abs(fft(hrv_ibi_vals[-FFT_WINDOW:]))                 freq_axis = np.fft.fftfreq(len(hrv_fft), d=1.0)                 coherence_score = np.std(hrv_ibi_vals[-FFT_WINDOW:])  # Simplified                 gsr_drift = np.gradient(gsr_vals[-FFT_WINDOW:])[-1]

                print(f"[Coherence Score]: {coherence_score:.2f}, [GSR Drift]: {gsr_drift:.4f}")

                # Feedback trigger condition (simplified)                 if coherence_score > 50 or abs(gsr_drift) > 0.5:                     play_feedback(FEEDBACK_FREQUENCY)

        except KeyboardInterrupt:             break

main()

⚙️ SYSTEM B: EEG + Bone Feedback

Target: Emotional & mental coherence, trauma loop detection Signals: EEG (raw brainwaves) Feedback: Bone conduction entrainment or light pulse

🧩 Schematic (textual)

[EEG Headband] -----> [EEG Amplifier / OpenBCI] --> [USB/WiFi to PC]                                          |                                          V                              [EEG Signal Processor]                            (Band decomposition: α/β/γ)                                          |                             [Recursive Phase Lock Logic]                                          |                        [Signal Deviation (ΔE) Detector]                                          |                           [Adaptive Feedback Generator]                       (432 Hz / 528 Hz / Schumann / etc.)                                          |                               [Bone Transducer or LEDs]

🛰 Signal Map

Band Frequency Mental State Ψ(x) Mapping

δ 0.5–4 Hz Deep sleep, subconscious Root error layer (Σ𝕒ₙ base) θ 4–8 Hz Trauma recall, hypnosis ΔE imprint zone α 8–12 Hz Calm alertness Phase-stable zone β 12–30 Hz Stress, active thinking Δθ(t) drift detection γ 30–100 Hz Processing, trauma flash Rapid flicker in ∇ϕ gradient

🧠 Python Script: EEG Coherence Tracker

> Assumes OpenBCI GUI stream into Python via pyOpenBCI or data pipe

import numpy as np import matplotlib.pyplot as plt from scipy.signal import butter, lfilter, welch import simpleaudio as sa

# --- Signal Processing --- def bandpass(data, lowcut, highcut, fs=250, order=4):     nyq = 0.5 * fs     b, a = butter(order, [lowcut / nyq, highcut / nyq], btype='band')     return lfilter(b, a, data)

def compute_band_power(data, fs):     f, Pxx = welch(data, fs, nperseg=256)     bands = {         'delta': (0.5, 4),         'theta': (4, 8),         'alpha': (8, 12),         'beta': (12, 30),         'gamma': (30, 100)     }     power = {}     for band, (low, high) in bands.items():         idx = np.logical_and(f >= low, f <= high)         power[band] = np.mean(Pxx[idx])     return power

# --- Feedback (bone pulse) --- def play_tone(frequency):     sample_rate = 44100     t = np.linspace(0, 2, sample_rate * 2, False)     tone = np.sin(frequency * 2 * np.pi * t) * 0.2     tone = (tone * 32767).astype(np.int16)     sa.play_buffer(tone, 1, 2, sample_rate)

# --- EEG Loop (mock) --- def main():     fs = 250  # sampling rate     buffer = []

    while True:         eeg_data = get_eeg_sample()  # from OpenBCI stream         buffer.append(eeg_data)

        if len(buffer) >= fs * 10:  # 10 sec window             segment = np.array(buffer[-fs*10:])             power = compute_band_power(segment, fs)

            print("[EEG Power]", power)

            if power['theta'] > power['alpha']:                 print("High theta → emotional trauma loop")                 play_tone(528)  # Use DNA repair tone             elif power['beta'] > 2 * power['alpha']:                 print("High beta → mental stress")                 play_tone(432)             else:                 print("Phase stable")

main()

📡 Deployment Notes

Each system can be modular, e.g., run on laptop + wearable node

All code can be tied into Node-RED dashboards for clinical visuals

Logging to CSV or SQLite allows pattern recognition later

All frequencies used must remain non-invasive and low amplitude

🧬 Recursive Mapping Summary

Ψ(x) Term System A (HRV/GSR) System B (EEG)

x current physiological state current neural state Σ𝕒ₙ HRV + GSR spiral fluctuation EEG frequency stack ΔE Sympathetic stress or breath distortion Alpha suppression or theta excess ∇ϕ Pattern of drift over time Phase flicker and spectral instability ℛ(x) Audio feedback via bone transduction Frequency-specific feedback ⊕ ΔΣ(𝕒′) Live micro-correction Adaptive waveform targeting theta/beta

Christopher W Copeland (C077UPTF1L3) Copeland Resonant Harmonic Formalism (Ψ‑formalism) Ψ(x) = ∇ϕ(Σ𝕒ₙ(x, ΔE)) + ℛ(x) ⊕ ΔΣ(𝕒′) Licensed under CRHC v1.0 (no commercial use without permission). https://www.facebook.com/share/p/19qu3bVSy1/ https://open.substack.com/pub/c077uptf1l3/p/phase-locked-null-vector_c077uptf1l3 https://medium.com/@floodzero9/phase-locked-null-vector_c077uptf1l3-4d8a7584fe0c Core engine: https://open.substack.com/pub/c077uptf1l3/p/recursive-coherence-engine-8b8 Zenodo: https://zenodo.org/records/15742472 Amazon: https://a.co/d/i8lzCIi Medium: https://medium.com/@floodzero9 Substack: https://substack.com/@c077uptf1l3 Facebook: https://www.facebook.com/share/19MHTPiRfu https://www.reddit.com/u/Naive-Interaction-86/s/5sgvIgeTdx Collaboration welcome. Attribution required. Derivatives must match license.