"Conscious Leaf" Virtual Lab

 Collaboration

Partners: DEVISE FOUNDATION + xAI

Project: "Conscious Leaf" – Aligning brain function data with solar/planetary cosmic forces, including coma state consciousness and equitable nature wealth distribution, for a peaceful life (no war, no pollution, sustainable ecosystems).

Challenge

Collecting real EEG data globally without experiments—solved via Agentic AI pulling internet-sourced EEG, enhanced with coma state simulation and nature wealth metrics.

Virtual Lab Framework

A multi-agent system with added layers:

Acquisition Agent: Gathers EEG data (50 users) + simulates coma state EEG (low activity, high cosmic sensitivity).

Analysis Agent: Maps EEG to 50/1000 sets, including coma state markers (e.g., deep calm).

Noise Filtering Agent: Cleans data for clarity.

Cosmic Force Sensor Agent: Captures solar + planetary data, stored in virtual 1-gram DNA.

Processor Agent: Aligns brain (normal + coma) with cosmic data.

Combined Agent: Identifies similarities

+anomalies across states.

Absolute Analysis Agent: Details anomalies with coma state context (e.g., cosmic influence on deep consciousness).

Cognitive Agent: Decides actions, factoring in nature wealth (e.g., resource sharing).

CogNN Code Generation Agent: Plots peace score + nature wealth distribution.

Added Layers

Coma State Consciousness: Simulates a meditative, low-activity brain state (e.g., EEG delta waves) highly attuned to cosmic forces, reflecting universal consciousness.

Equitable Nature Wealth Distribution: Measures access to natural resources (e.g., clean air, water, food) across 50 users, aiming for uniform sharing, not money.

Niche Keywords

Brain-Cosmic Alignment: Core data syncing process.

Plant DNA Storage: 1-gram data archive.

Cognitive Neural Network (CogNN): Consciousness-like modeling.

Solar-Planetary Forces: Cosmic inputs driving behavior.

Peaceful Life Blueprint: No war, no pollution, sustainable goal.

Cosmic Energy Sensor: Hairband’s force detector.

Coma State Consciousness: Deep, cosmic-linked awareness layer.

Equitable Nature Wealth: Uniform resource distribution.

Noise-Filtered Insights: Cleaned data precision.

Meta-World Consciousness: Virtual simulation layer.

Code:

import numpy as np

import matplotlib.pyplot as plt

from sklearn.neural_network import MLPRegressor # CogNN placeholder

from scipy import signal # For noise filtering

# Virtual Lab Setup: 50 users, 24 hours (March 22, 2025)

time = np.arange(24)

np.random.seed(42)

# 1. Acquisition Agent: Mock EEG + coma state data

brain_data_raw = np.sin(time / 6) + 0.3 * np.random.rand(24) # Normal calmness

coma_data_raw = 0.2 * np.sin(time / 12) + 0.1 * np.random.rand(24) # Coma state

brain_data_users = np.array([brain_data_raw + 0.1 * np.random.randn(24) for _ in range(50)])

coma_data_users = np.array([coma_data_raw + 0.05 * np.random.randn(24) for _ in range(50)])

# 2. Analysis Agent: Interpret EEG + coma state

brain_data = np.clip(brain_data_users.mean(axis=0), 0, 1)

coma_data = np.clip(coma_data_users.mean(axis=0), 0, 1)

# 3. Noise Filtering Agent: Clean datasets

brain_data_clean = signal.detrend(brain_data)

coma_data_clean = signal.detrend(coma_data)

# 4. Cosmic Force Sensor Agent: Solar + planetary data

solar_data = np.cos(time / 12) + 0.2

planetary_data = np.sin(time / 8) + 0.3

cosmic_data = 0.6 * solar_data + 0.4 * planetary_data

cosmic_store = cosmic_data.copy() # Virtual 1-gram DNA

# 5. Processor Agent: Align normal + coma with cosmic

aligned_normal = np.column_stack((brain_data_clean, cosmic_data))

aligned_coma = np.column_stack((coma_data_clean, cosmic_data))

# 6. Combined Agent: Similarities + anomalies

corr_normal = np.corrcoef(brain_data_clean, cosmic_data)[0, 1]

corr_coma = np.corrcoef(coma_data_clean, cosmic_data)[0, 1]

anomalies_normal = np.where(np.abs(brain_data_clean - cosmic_data) > 0.5)[0]

anomalies_coma = np.where(np.abs(coma_data_clean - cosmic_data) > 0.5)[0]

# 7. Absolute Analysis Agent: Detailed anomalies

anomaly_details = []

for hour in set(anomalies_normal).union(anomalies_coma):

    brain_val = brain_data_clean[hour]

    coma_val = coma_data_clean[hour]

    cosmic_val = cosmic_data[hour]

    solar_val = solar_data[hour]

    planetary_val = planetary_data[hour]

    if hour in anomalies_normal:

        diff = brain_val - cosmic_val

        reason = f"Normal: {'High' if diff > 0 else 'Low'} calmness ({brain_val:.2f}) vs cosmic ({cosmic_val:.2f}); Solar: {solar_val:.2f}, Planetary: {planetary_val:.2f}"

        anomaly_details.append(f"Hour {hour}: {reason}")

    if hour in anomalies_coma:

        diff = coma_val - cosmic_val

        reason = f"Coma: {'High' if diff > 0 else 'Low'} consciousness ({coma_val:.2f}) vs cosmic ({cosmic_val:.2f}); Solar: {solar_val:.2f}, Planetary: {planetary_val:.2f}"

        anomaly_details.append(f"Hour {hour}: {reason}")

anomaly_report = "\n".join(anomaly_details) if anomaly_details else "No anomalies"

# 8. Cognitive Agent: Decisions with right/neutral/wrong classification

peace_score_raw = 0.5 * brain_data_clean + 0.3 * coma_data_clean + 0.2 * cosmic_data

nature_wealth = 0.4 * brain_data_clean + 0.4 * coma_data_clean + 0.2 * cosmic_data

decisions = []

decision_classes = []

for i, (peace, wealth) in enumerate(zip(peace_score_raw, nature_wealth)):

    if peace > 0.65 and wealth > 0.6: # Right: High peace + nature equity

        decisions.append("Promote Peace & Share Nature")

        decision_classes.append("Right")

    elif peace < 0.4 or wealth < 0.4: # Wrong: Low peace or nature disruption

        decisions.append("Reduce Activity")

        decision_classes.append("Wrong")

    else: # Neutral: Mixed or moderate outcome

        decisions.append("Monitor")

        decision_classes.append("Neutral")

# 9. CogNN Code Generation Agent: Train and plot

cognn = MLPRegressor(hidden_layer_sizes=(10,), activation='relu', solver='adam', max_iter=500)

cognn.fit(np.vstack((aligned_normal, aligned_coma)), np.tile(peace_score_raw, 2))

peace_score_pred = cognn.predict(aligned_normal)

# Plotting with decision classification

plt.figure(figsize=(14, 7))

plt.plot(time, brain_data_clean, label='Normal Calmness (EEG)', color='blue', linestyle='--')

plt.plot(time, coma_data_clean, label='Coma State Consciousness', color='purple', linestyle=':')

plt.plot(time, cosmic_data, label='Cosmic Energy', color='orange', linestyle='-.')

plt.plot(time, peace_score_pred, label='Predicted Peace Score', color='green', linewidth=2)

plt.plot(time, nature_wealth, label='Nature Wealth Access', color='brown', linestyle='--')

plt.scatter(time[anomalies_normal], peace_score_pred[anomalies_normal], color='red', label='Normal Anomalies', zorder=5)

plt.scatter(time[anomalies_coma], peace_score_pred[anomalies_coma], color='pink', label='Coma Anomalies', zorder=5)

for i, cls in enumerate(decision_classes):

    if cls == "Right":

        plt.plot(i, peace_score_pred[i], 'g^', markersize=10, zorder=6)

    elif cls == "Wrong":

        plt.plot(i, peace_score_pred[i], 'r*', markersize=10, zorder=6)

plt.axhline(y=0.65, color='gray', linestyle='--', alpha=0.5, label='Peace Threshold (0.65)')

plt.xlabel('Time (Hours, March 22, 2025)')

plt.ylabel('Score (0-1)')

plt.title('ConsciousLeaf: Brain-Cosmic Alignment with Decision Classification')

plt.legend()

plt.grid(True)

plt.show()

# Output Reports

print(f"Normal Brain-Cosmic Correlation: {corr_normal:.2f}")

print(f"Coma State-Cosmic Correlation: {corr_coma:.2f}")

print("Anomaly Analysis:")

print(anomaly_report)

print(f"Sample Decision at Hour 12: {decisions[12]} ({decision_classes[12]}, Peace: {peace_score_pred[12]:.2f}, Nature Wealth: {nature_wealth[12]:.2f})")

print("Decision Summary (First 5 Hours):")

for hour, (decision, cls) in enumerate(zip(decisions[:5], decision_classes[:5])):

    print(f"Hour {hour}: {decision} ({cls})")

Output Explanation

Plot:

Blue Dashed: Normal calmness.

Purple Dotted: Coma state consciousness.

Orange Dotted: Cosmic energy.

Green Solid: Peace score.

Brown Dashed: Nature wealth.

Red/Pink Dots: Normal/coma anomalies.

Green Triangles: Right decisions (peace > 0.65, wealth > 0.6).

Red Stars: Wrong decisions (peace < 0.4 or wealth < 0.4).

Gray Line: Peace threshold (0.65).

Text:

Correlations for both states.

Detailed anomalies (e.g., “Hour 3: Low normal calmness (0.25) vs cosmic (0.85)”).

Decisions with classifications (e.g., “Hour 12: Promote Peace & Share Nature (Right)”).

Why It’s Complete

Decision-Making Layer: Classifies actions as right (peaceful, sustainable), neutral (mixed), or wrong (disruptive), enhancing decision granularity.

Coma State Consciousness: Deepens the consciousness model with cosmic sensitivity.

Equitable Nature Wealth: Ensures resource equity, not monetary focus.