🧬 Synaptica Q — Quantum Coherence Index (QCI)

Author: Luca Melli
License: Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Repository: github.com/lucamelli/Synaptica_Q

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🧠 Overview

Synaptica Q is a research framework that models and quantifies quantum-biological coherence in neural systems.
It introduces the Quantum Coherence Index (QCI), a metric derived from EEG signals that estimates the dynamic balance between coherence, entropy, and energetic input within cortical networks.

The QCI is governed by the differential equation:

Text form (for searchability):
dQCI(t)/dt = α·E_in(t) − β·S(t) + γ·C_ent(t) − κ·QCI(t)

where:

Symbol	Meaning
Ein(t)	Normalized cortical energetic input (α-band power)
S(t)	Spectral entropy (measure of disorder)
Cent(t)	Magnitude-squared coherence between EEG channels
QCI(t)	Damping term representing information dissipation

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⚙️ Algorithms

File	Description
synaptica_qci.py	Main implementation of the QCI model — computes α, β, γ, κ and fits them to EEG data.
synaptica_qci_lagsboot.py	Lag optimization and bootstrap validation of parameters; generates diagnostic plots and summary CSV.

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📂 Output Structure

Each subject folder (e.g. Subject 0, Subject 1, …) contains:

File	Description
qci_timeseries.png	Normalized features and QCI curve
qci_corr.png	Feature correlation matrix
qci_lagsboot_fit_train.png	Model fit on training data
qci_lagsboot_params_summary.csv	Parameter table with α, β, γ, κ, corr_test, rmse

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🧪 EEG Dataset

Validated using PhysioNet — EEG During Mental Arithmetic Tasks
https://physionet.org/content/eegmat/1.0.0/

Channels analyzed: C3, Cz, C4, Pz, P3, P4 (central-parietal cortex).

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🔍 Reproducibility

I use fixed random seeds and list exact library versions in requirements.txt.
The analyses were developed and tested on macOS (Apple Silicon) with Python 3.12.
For reproducibility, the reader can re-run the scripts in the same environment.

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🧾 Privacy & Ethics

All datasets used are public and de-identified (e.g., PhysioNet EEGMAT).
No personal or sensitive information is recorded or shared in this repository.
This work is intended strictly for non-commercial scientific research.

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⚠️ Limitations

• This is a proof-of-concept pipeline tested on publicly available EEG data;
• The QCI index is hypothetical and must be validated on broader datasets,
  including multi-modal and clinical populations;
• Noise, artifacts, and inter-individual variability may affect model stability;
• The current implementation uses coherence as a fallback (mne.connectivity not available),
  which is a simplification of the intended connectivity measure.

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📦 Dependencies

Install the required Python packages:

git clone https://github.com/lucamelli/Synaptica_Q.git
cd Synaptica_Q
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python synaptica_qci.py