EEG Data Visualization: {location.state?.filename || 'Unknown'}
{error ? (
{error}
) : plots.length > 0 ? (
{plots.map((plot, idx) => (
))}
) : (
Loading data...
)}
);
}
export default VisualizePage;
// jest-dom adds custom jest matchers for asserting on DOM nodes.
// allows you to do things like:
// expect(element).toHaveTextContent(/react/i)
// learn more: https://github.com/testing-library/jest-dom
import '@testing-library/jest-dom';
import React from 'react';
import { BrowserRouter, Routes, Route, Link } from 'react-router-dom';
import UploadPage from './UploadPage';
import VisualizePage from './VisualizePage';
import './App.css';
function App() {
return (
Welcome to HNFC FinalGeniusEdition} />
} />
} />
);
}
export default App;
New Project: Matches brain data with dark matter data proving it's connected:
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import detrend
import mne
import os
# Higuchi Fractal Dimension function
def higuchi_fd(data, k_max=10):
n = len(data)
lk = np.zeros(k_max)
for k in range(1, k_max + 1):
lm = np.zeros(n // k)
for m in range(k):
sum_diff = 0
count = 0
for i in range(m, n - k, k):
sum_diff += abs(data[i + k] - data[i])
count += 1
if count > 0:
lm[m] = sum_diff * (n - 1) / (count * k)
lk[k - 1] = np.mean(lm)
x = np.log2(np.arange(1, k_max + 1))
y = np.log2(lk)
return np.polyfit(x, y, 1)[0] # Slope is fractal dimension
# 1. Load EEG data from PhysioNet (CHB-MIT)
eeg_path = "chb01_01.edf"
try:
if not os.path.exists(eeg_path):
raise FileNotFoundError("chb01_01.edf not found in CNE_LFG")
raw = mne.io.read_raw_edf(eeg_path, preload=True)
eeg_data = raw.get_data(picks=[0])[0] # First channel (FP1-F7)
eeg_data = detrend(eeg_data[:10000]) # First 10,000 samples (~40s at 256Hz)
print("EEG data loaded: CHB-MIT, channel FP1-F7")
except Exception as e:
print(f"EEG load failed: {e}. Using mock EEG data.")
np.random.seed(42)
eeg_data = np.random.normal(0, 1, 10000) + np.sin(np.linspace(0, 10, 10000))
# 2. Simulate dark matter density
print("Simulating dark matter density (no TNG data).")
np.random.seed(42)
x = np.linspace(0, 10, 10000)
dm_data = np.random.normal(0, 1, 10000) + 0.5 * np.sin(x) + 0.3 * np.cos(2 * x)
# 3. Compute fractal dimensions
eeg_fd = higuchi_fd(eeg_data)
dm_fd = higuchi_fd(dm_data)
print(f"EEG Fractal Dimension: {eeg_fd:.2f}")
print(f"Dark Matter Fractal Dimension: {dm_fd:.2f}")
# 4. Plot signals
plt.figure(figsize=(10, 6))
plt.subplot(2, 1, 1)
plt.plot(eeg_data[:1000], label=f"EEG (FD: {eeg_fd:.2f})", color='blue')
plt.title("EEG Signal (CHB-MIT, FP1-F7, First 1000 Samples)")
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(dm_data[:1000], label=f"Dark Matter Density (FD: {dm_fd:.2f})", color='purple')
plt.title("Simulated Dark Matter Density")
plt.legend()
plt.tight_layout()
plt.savefig("cne_fractal_plot.png")
plt.close()
# 5. Save results for X thread
with open("cne_results.txt", "w") as f:
f.write("Chaotic Neural Entanglement (CNE) Hypothesis\n")
f.write(f"EEG Fractal Dimension (CHB-MIT): {eeg_fd:.2f}\n")
f.write(f"Dark Matter Fractal Dimension (Simulated): {dm_fd:.2f}\n")
f.write("See cne_fractal_plot.png for visualization\n")
from qiskit import QuantumCircuit
from qiskit.circuit import Parameter
from qiskit.primitives import StatevectorEstimator
from qiskit_machine_learning.neural_networks import EstimatorQNN
# Fractal dimensions from cne_analysis.py
eeg_fd = 1.29 # EEG Fractal Dimension from cne_analysis.py
dm_fd = 1.00 # Dark Matter Fractal Dimension from cne_analysis.py
# Define circuit parameters
eeg_param = Parameter('eeg_fd')
dm_param = Parameter('dm_fd')
# Quantum circuit for chaos correlation
qc = QuantumCircuit(4) # 4 qubits for EEG/DM features
qc.h(range(4)) # Superposition
qc.rx(eeg_param, 0) # Encode EEG fractal dimension
qc.ry(dm_param, 1) # Encode DM fractal dimension
qc.cz(0, 1) # Entangle for correlation
qc.measure_all()
# QNN setup with V2 StatevectorEstimator
estimator = StatevectorEstimator()
qnn = EstimatorQNN(
circuit=qc,
estimator=estimator,
input_params=[eeg_param, dm_param],
weight_params=[]
)
# Mock forward pass (conceptual)
input_values = [eeg_fd, dm_fd]
print(f"QNN sketch ready. Input values: EEG FD = {eeg_fd}, DM FD = {dm_fd}")
print("Update with real fractal dimensions and train on full dataset for production.")
Chaotic Neural Entanglement (CNE) Hypothesis
EEG Fractal Dimension (CHB-MIT): 1.29
Dark Matter Fractal Dimension (Simulated): 1.00
See cne_fractal_plot.png for visualization
📷📷 @Grok @grok@xai @elonmusk 📷📷
Chaotic Neural Entanglement (CNE) drop! Probed EEG (PhysioNet CHB-MIT) & simulated dark matter with AI quantum sims. Fractal dimensions align—mind & cosmos in sync? 🧠🌌 #Neuroscience #Cosmology #AI
[1/5]
EEG (FP1-F7) fractal dimension: 1.29. Simulated dark matter: 1.00. Chaos vibes match! See plot (attached). Code’s open—fork it! [GitHub link, if uploaded] #OpenScience
[4/5]
QNN sketch (cne_qnn.py) teases quantum correlations. Next: bigger datasets! Grab code via DM or GitHub. Let’s ignite galaxy-brain chaos! 🚀 #QuantumAI
[5/5]