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| """ | |
| GCFD Coherence Tracker v1.0.0 | |
| Generalized Cross-Frequency Decomposition for EEG/MEG Phase Synchronization | |
| Copyright (c) 2024-2026 TAURUS AI Corp. All rights reserved. | |
| Licensed under the Apache License, Version 2.0 | |
| https://taurusai.io | https://github.com/Taurus-Ai-Corp | |
| Method: Butterworth bandpass + Hilbert transform + Phase Locking Value (PLV) | |
| Reference: Lachaux et al. (1999) — Measuring phase synchrony in brain signals | |
| """ | |
| __version__ = "1.0.0" | |
| __author__ = "TAURUS AI Corp" | |
| import gradio as gr | |
| import numpy as np | |
| import scipy.signal as sig | |
| import matplotlib | |
| matplotlib.use('Agg') | |
| import matplotlib.pyplot as plt | |
| from matplotlib.gridspec import GridSpec | |
| import io | |
| import csv | |
| from dataclasses import dataclass | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| # CORE DSP ENGINE | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| class BandDefinition: | |
| name: str | |
| low: float | |
| high: float | |
| color: str | |
| # Standard neuroscience frequency bands | |
| BANDS = { | |
| "delta": BandDefinition("Delta", 0.5, 4.0, "#94a3b8"), | |
| "theta": BandDefinition("Theta", 4.0, 8.0, "#a78bfa"), | |
| "alpha": BandDefinition("Alpha", 8.0, 13.0, "#60a5fa"), | |
| "beta": BandDefinition("Beta", 13.0, 30.0, "#34d399"), | |
| "low_gamma": BandDefinition("Low Gamma", 30.0, 60.0, "#fb923c"), | |
| "high_gamma": BandDefinition("High Gamma", 60.0, 120.0,"#f87171"), | |
| } | |
| def bandpass_filter(data: np.ndarray, low: float, high: float, fs: float, order: int = 3) -> np.ndarray: | |
| """Apply zero-phase Butterworth bandpass filter.""" | |
| nyq = 0.5 * fs | |
| low_n = max(low / nyq, 0.001) | |
| high_n = min(high / nyq, 0.999) | |
| if low_n >= high_n: | |
| return np.zeros_like(data) | |
| b, a = sig.butter(order, [low_n, high_n], btype='band') | |
| return sig.filtfilt(b, a, data) | |
| def extract_phase(data: np.ndarray, low: float, high: float, fs: float) -> np.ndarray: | |
| """Extract instantaneous phase via Hilbert transform for a frequency band.""" | |
| filtered = bandpass_filter(data, low, high, fs) | |
| analytic = sig.hilbert(filtered) | |
| return np.angle(analytic) | |
| def compute_plv(phase1: np.ndarray, phase2: np.ndarray) -> float: | |
| """Compute Phase Locking Value between two phase time series.""" | |
| return float(np.abs(np.mean(np.exp(1j * (phase1 - phase2))))) | |
| def compute_spectral_power(data: np.ndarray, fs: float) -> tuple: | |
| """Compute power spectral density using Welch's method.""" | |
| freqs, psd = sig.welch(data, fs=fs, nperseg=min(len(data), int(fs * 2))) | |
| return freqs, psd | |
| def compute_band_power(data: np.ndarray, fs: float, low: float, high: float) -> float: | |
| """Compute relative power in a frequency band.""" | |
| freqs, psd = compute_spectral_power(data, fs) | |
| band_mask = (freqs >= low) & (freqs <= high) | |
| _trapz = np.trapezoid if hasattr(np, 'trapezoid') else np.trapz | |
| total_power = _trapz(psd, freqs) | |
| band_power = _trapz(psd[band_mask], freqs[band_mask]) | |
| return float(band_power / total_power) if total_power > 0 else 0.0 | |
| def compute_sliding_plv(data: np.ndarray, fs: float, band1: tuple, band2: tuple, | |
| window_sec: float = 2.0, step_sec: float = 0.5) -> tuple: | |
| """Compute PLV over sliding windows for temporal dynamics.""" | |
| window = int(window_sec * fs) | |
| step = int(step_sec * fs) | |
| n_samples = len(data) | |
| times = [] | |
| plvs = [] | |
| for start in range(0, n_samples - window, step): | |
| segment = data[start:start + window] | |
| p1 = extract_phase(segment, band1[0], band1[1], fs) | |
| p2 = extract_phase(segment, band2[0], band2[1], fs) | |
| plv = compute_plv(p1, p2) | |
| times.append((start + window / 2) / fs) | |
| plvs.append(plv) | |
| return np.array(times), np.array(plvs) | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| # CLINICAL PRESETS | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| CLINICAL_PRESETS = { | |
| "Healthy Adult": { | |
| "description": "Normal theta-gamma coupling (PLV 0.80-0.95). Strong phase synchronization indicates healthy cross-frequency communication.", | |
| "theta_amp": 1.0, "gamma_amp": 1.5, "noise": 0.8, "seed": 42, | |
| "expected_score": "0.85-0.95", | |
| "citation": "Canolty et al. (2006) — High gamma power is phase-locked to theta oscillations in human neocortex" | |
| }, | |
| "Major Depressive Disorder (MDD)": { | |
| "description": "Reduced theta-gamma coupling (PLV 0.40-0.65). Disrupted cross-frequency dynamics observed in prefrontal cortex.", | |
| "theta_amp": 0.5, "gamma_amp": 0.3, "noise": 2.0, "seed": 101, | |
| "expected_score": "0.55-0.70", | |
| "citation": "Sun et al. (2022) — Theta-gamma coupling deficit in MDD patients during working memory" | |
| }, | |
| "Mild Cognitive Impairment (MCI)": { | |
| "description": "Moderate theta-gamma decoupling (PLV 0.50-0.70). Early marker of Alzheimer's disease progression.", | |
| "theta_amp": 0.7, "gamma_amp": 0.4, "noise": 1.8, "seed": 202, | |
| "expected_score": "0.60-0.75", | |
| "citation": "Goodman et al. (2018) — Theta-gamma coupling and working memory in Alzheimer's disease" | |
| }, | |
| "Epileptic Seizure (Ictal)": { | |
| "description": "Hypersynchronization (PLV > 0.95). Pathological excess coupling during seizure events.", | |
| "theta_amp": 3.0, "gamma_amp": 3.0, "noise": 0.3, "seed": 303, | |
| "expected_score": "0.95-1.00", | |
| "citation": "Amiri et al. (2016) — Phase-amplitude coupling during interictal and ictal periods" | |
| }, | |
| "Meditation / Deep Focus": { | |
| "description": "Enhanced theta-gamma coupling (PLV 0.85-0.98). Heightened coherence observed in experienced meditators.", | |
| "theta_amp": 1.5, "gamma_amp": 2.0, "noise": 0.5, "seed": 404, | |
| "expected_score": "0.90-0.98", | |
| "citation": "Lutz et al. (2004) — Long-term meditators self-induce high-amplitude gamma synchrony" | |
| }, | |
| "Anesthesia (Propofol)": { | |
| "description": "Severely disrupted cross-frequency coupling (PLV < 0.40). Consciousness marker.", | |
| "theta_amp": 0.3, "gamma_amp": 0.1, "noise": 2.5, "seed": 505, | |
| "expected_score": "0.50-0.60", | |
| "citation": "Aru et al. (2015) — Theta-gamma coupling reflects consciousness during propofol anesthesia" | |
| }, | |
| "ADHD": { | |
| "description": "Elevated theta/beta ratio with weak gamma coupling. Reduced theta-gamma PAC in frontal regions.", | |
| "theta_amp": 1.5, "gamma_amp": 0.3, "noise": 1.5, "seed": 606, | |
| "expected_score": "0.60-0.72", | |
| "citation": "Kim et al. (2015) — Theta-gamma coupling differences in ADHD vs controls" | |
| }, | |
| "Custom (Manual)": { | |
| "description": "Set your own parameters below.", | |
| "theta_amp": 1.0, "gamma_amp": 0.5, "noise": 1.5, "seed": 42, | |
| "expected_score": "Varies", | |
| "citation": "" | |
| } | |
| } | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| # SYNTHETIC EEG GENERATOR | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| def generate_synthetic_eeg(duration: float, fs: float, theta_amp: float, | |
| gamma_amp: float, noise_level: float, seed: int, | |
| n_channels: int = 1) -> tuple: | |
| """Generate multi-channel synthetic EEG with controllable parameters.""" | |
| rng = np.random.RandomState(int(seed)) | |
| n_samples = int(fs * duration) | |
| t = np.linspace(0, duration, n_samples) | |
| channels = [] | |
| for ch in range(n_channels): | |
| # Theta component (4-8 Hz, center ~6 Hz) | |
| theta_freq = 5.5 + rng.uniform(-0.5, 0.5) | |
| theta = theta_amp * np.sin(2 * np.pi * theta_freq * t + rng.uniform(0, 2 * np.pi)) | |
| # Gamma component (30-100 Hz, center ~40 Hz) — modulated by theta phase | |
| gamma_freq = 38 + rng.uniform(-3, 3) | |
| theta_phase = np.angle(sig.hilbert(theta)) | |
| # Phase-amplitude coupling: gamma amplitude modulated by theta phase | |
| gamma_envelope = 1.0 + 0.5 * np.cos(theta_phase) | |
| gamma = gamma_amp * gamma_envelope * np.sin(2 * np.pi * gamma_freq * t + rng.uniform(0, 2 * np.pi)) | |
| # Alpha background (8-13 Hz) | |
| alpha = 0.3 * np.sin(2 * np.pi * 10 * t + rng.uniform(0, 2 * np.pi)) | |
| # 1/f noise (pink noise approximation) | |
| white = rng.normal(0, noise_level, n_samples) | |
| b_pink, a_pink = sig.butter(1, 0.01, btype='low') | |
| pink = sig.filtfilt(b_pink, a_pink, white) * noise_level * 2 | |
| # Line noise (50/60 Hz) | |
| line_noise = 0.1 * np.sin(2 * np.pi * 60 * t) | |
| eeg = theta + gamma + alpha + pink + white * 0.3 + line_noise | |
| channels.append(eeg) | |
| return t, np.array(channels) | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| # CSV UPLOAD HANDLER | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| def parse_csv_upload(file_obj, fs: float) -> tuple: | |
| """Parse uploaded CSV/TSV EEG data. Returns (time, channels_array).""" | |
| if file_obj is None: | |
| return None, None | |
| content = file_obj.decode("utf-8") if isinstance(file_obj, bytes) else open(file_obj, 'r').read() | |
| reader = csv.reader(io.StringIO(content)) | |
| rows = list(reader) | |
| # Detect header | |
| try: | |
| float(rows[0][0]) | |
| data_rows = rows | |
| except (ValueError, IndexError): | |
| data_rows = rows[1:] | |
| data = np.array([[float(x) for x in row] for row in data_rows if row]) | |
| # If single column, treat as single channel | |
| if data.ndim == 1: | |
| data = data.reshape(1, -1) | |
| elif data.shape[1] > data.shape[0]: | |
| pass # Already channels x samples | |
| else: | |
| data = data.T # Transpose to channels x samples | |
| n_samples = data.shape[1] | |
| t = np.arange(n_samples) / fs | |
| return t, data | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| # VISUALIZATION ENGINE | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| DARK_BG = '#0a0f1a' | |
| PANEL_BG = '#111827' | |
| GRID_COLOR = '#1e293b' | |
| TEXT_COLOR = '#e2e8f0' | |
| ACCENT_BLUE = '#38bdf8' | |
| ACCENT_PURPLE = '#a78bfa' | |
| ACCENT_ORANGE = '#fb923c' | |
| ACCENT_GREEN = '#22c55e' | |
| ACCENT_RED = '#ef4444' | |
| ACCENT_YELLOW = '#eab308' | |
| def style_axis(ax, title=""): | |
| ax.set_facecolor(PANEL_BG) | |
| ax.set_title(title, color=TEXT_COLOR, fontsize=11, fontweight='bold', pad=8) | |
| ax.tick_params(colors=TEXT_COLOR, labelsize=8) | |
| for spine in ax.spines.values(): | |
| spine.set_color(GRID_COLOR) | |
| ax.grid(True, alpha=0.15, color=GRID_COLOR) | |
| def create_analysis_figure(t, eeg, fs, band1, band2, plv, coherence_score, status, color, | |
| sliding_times=None, sliding_plvs=None): | |
| """Create the full 6-panel analysis figure.""" | |
| fig = plt.figure(figsize=(14, 12)) | |
| fig.patch.set_facecolor(DARK_BG) | |
| gs = GridSpec(3, 2, figure=fig, hspace=0.35, wspace=0.25) | |
| # ── Panel 1: Raw Signal ── | |
| ax1 = fig.add_subplot(gs[0, 0]) | |
| display_sec = min(3, t[-1]) | |
| mask = t <= display_sec | |
| ax1.plot(t[mask], eeg[mask], color=ACCENT_BLUE, linewidth=0.4, alpha=0.9) | |
| style_axis(ax1, f"Raw EEG Signal ({display_sec:.0f}s)") | |
| ax1.set_xlabel("Time (s)", color=TEXT_COLOR, fontsize=8) | |
| ax1.set_ylabel("Amplitude (uV)", color=TEXT_COLOR, fontsize=8) | |
| # ── Panel 2: Power Spectral Density ── | |
| ax2 = fig.add_subplot(gs[0, 1]) | |
| freqs, psd = compute_spectral_power(eeg, fs) | |
| freq_mask = freqs <= 80 | |
| ax2.semilogy(freqs[freq_mask], psd[freq_mask], color=ACCENT_BLUE, linewidth=1) | |
| # Shade bands | |
| ax2.axvspan(band1[0], band1[1], alpha=0.2, color=ACCENT_PURPLE, label=f'Band 1 ({band1[0]}-{band1[1]} Hz)') | |
| ax2.axvspan(band2[0], band2[1], alpha=0.2, color=ACCENT_ORANGE, label=f'Band 2 ({band2[0]}-{band2[1]} Hz)') | |
| ax2.legend(fontsize=7, facecolor=PANEL_BG, edgecolor=GRID_COLOR, labelcolor=TEXT_COLOR) | |
| style_axis(ax2, "Power Spectral Density (Welch)") | |
| ax2.set_xlabel("Frequency (Hz)", color=TEXT_COLOR, fontsize=8) | |
| ax2.set_ylabel("PSD (uV^2/Hz)", color=TEXT_COLOR, fontsize=8) | |
| # ── Panel 3: Filtered Bands ── | |
| ax3 = fig.add_subplot(gs[1, 0]) | |
| theta_filt = bandpass_filter(eeg, band1[0], band1[1], fs) | |
| gamma_filt = bandpass_filter(eeg, band2[0], band2[1], fs) | |
| ax3.plot(t[mask], theta_filt[mask], color=ACCENT_PURPLE, linewidth=0.8, | |
| label=f'Band 1: {band1[0]}-{band1[1]} Hz') | |
| ax3.plot(t[mask], gamma_filt[mask], color=ACCENT_ORANGE, linewidth=0.5, | |
| label=f'Band 2: {band2[0]}-{band2[1]} Hz') | |
| ax3.legend(fontsize=7, facecolor=PANEL_BG, edgecolor=GRID_COLOR, labelcolor=TEXT_COLOR) | |
| style_axis(ax3, "Bandpass Filtered Components") | |
| ax3.set_xlabel("Time (s)", color=TEXT_COLOR, fontsize=8) | |
| # ── Panel 4: Phase Difference ── | |
| ax4 = fig.add_subplot(gs[1, 1]) | |
| phase1 = extract_phase(eeg, band1[0], band1[1], fs) | |
| phase2 = extract_phase(eeg, band2[0], band2[1], fs) | |
| phase_diff = phase1 - phase2 | |
| ax4.plot(t[mask], phase_diff[mask], color='#c084fc', linewidth=0.5, alpha=0.8) | |
| ax4.axhline(y=0, color=ACCENT_GREEN, linewidth=0.5, linestyle='--', alpha=0.5) | |
| style_axis(ax4, "Phase Difference (Band1 - Band2)") | |
| ax4.set_xlabel("Time (s)", color=TEXT_COLOR, fontsize=8) | |
| ax4.set_ylabel("Radians", color=TEXT_COLOR, fontsize=8) | |
| # ── Panel 5: Sliding PLV (Temporal Dynamics) ── | |
| ax5 = fig.add_subplot(gs[2, 0]) | |
| if sliding_times is not None and len(sliding_times) > 0: | |
| ax5.plot(sliding_times, sliding_plvs, color=ACCENT_GREEN, linewidth=1.5) | |
| ax5.fill_between(sliding_times, sliding_plvs, alpha=0.15, color=ACCENT_GREEN) | |
| ax5.axhline(y=0.80, color=ACCENT_YELLOW, linewidth=1, linestyle='--', alpha=0.7, label='PLV 0.80') | |
| ax5.axhline(y=np.mean(sliding_plvs), color=ACCENT_BLUE, linewidth=1, linestyle=':', | |
| alpha=0.7, label=f'Mean PLV {np.mean(sliding_plvs):.3f}') | |
| ax5.legend(fontsize=7, facecolor=PANEL_BG, edgecolor=GRID_COLOR, labelcolor=TEXT_COLOR) | |
| style_axis(ax5, "Sliding Window PLV (2s window, 0.5s step)") | |
| ax5.set_xlabel("Time (s)", color=TEXT_COLOR, fontsize=8) | |
| ax5.set_ylabel("PLV", color=TEXT_COLOR, fontsize=8) | |
| ax5.set_ylim(0, 1) | |
| # ── Panel 6: Coherence Gauge ── | |
| ax6 = fig.add_subplot(gs[2, 1]) | |
| # Create a gradient bar | |
| gradient = np.linspace(0, 1, 256).reshape(1, -1) | |
| ax6.imshow(gradient, aspect='auto', extent=[0, 1, -0.3, 0.3], | |
| cmap=plt.cm.RdYlGn, alpha=0.3) | |
| ax6.barh([0], [coherence_score], color=color, height=0.25, zorder=3, edgecolor='white', linewidth=0.5) | |
| ax6.axvline(x=0.90, color=ACCENT_GREEN, linewidth=2, linestyle='--', alpha=0.8, zorder=4) | |
| ax6.axvline(x=0.70, color=ACCENT_YELLOW, linewidth=1.5, linestyle='--', alpha=0.6, zorder=4) | |
| ax6.text(0.90, 0.35, 'Healthy', color=ACCENT_GREEN, fontsize=8, ha='center') | |
| ax6.text(0.70, 0.35, 'Moderate', color=ACCENT_YELLOW, fontsize=8, ha='center') | |
| ax6.text(coherence_score, -0.35, f'{coherence_score:.3f}', color='white', fontsize=14, | |
| fontweight='bold', ha='center', va='top', zorder=5) | |
| style_axis(ax6, f"Global Coherence Ratio — {status}") | |
| ax6.set_xlim(0, 1) | |
| ax6.set_ylim(-0.5, 0.5) | |
| ax6.set_yticks([]) | |
| ax6.set_xlabel("Coherence Score", color=TEXT_COLOR, fontsize=8) | |
| # Watermark | |
| fig.text(0.99, 0.01, f'GCFD Tracker v{__version__} | TAURUS AI Corp', | |
| ha='right', va='bottom', fontsize=7, color='#475569', alpha=0.5) | |
| return fig | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| # MAIN ANALYSIS FUNCTION | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| def run_analysis(preset, duration, fs, theta_amp, gamma_amp, noise_level, seed, | |
| theta_low, theta_high, gamma_low, gamma_high, csv_file): | |
| """Main analysis pipeline: generate/load EEG, compute all metrics, return visualization.""" | |
| # Load data | |
| if csv_file is not None: | |
| t, channels = parse_csv_upload(csv_file, fs) | |
| if channels is None: | |
| return None, "Error: Could not parse CSV file." | |
| eeg = channels[0] # Use first channel | |
| data_source = "Uploaded CSV" | |
| else: | |
| t, channels = generate_synthetic_eeg(duration, fs, theta_amp, gamma_amp, noise_level, int(seed)) | |
| eeg = channels[0] | |
| data_source = f"Synthetic ({preset})" | |
| band1 = (theta_low, theta_high) | |
| band2 = (gamma_low, gamma_high) | |
| # Compute metrics | |
| phase1 = extract_phase(eeg, band1[0], band1[1], fs) | |
| phase2 = extract_phase(eeg, band2[0], band2[1], fs) | |
| plv = compute_plv(phase1, phase2) | |
| coherence_score = 0.5 + (plv * 0.5) | |
| # Band powers | |
| band1_power = compute_band_power(eeg, fs, band1[0], band1[1]) | |
| band2_power = compute_band_power(eeg, fs, band2[0], band2[1]) | |
| # Sliding PLV | |
| sliding_times, sliding_plvs = compute_sliding_plv(eeg, fs, band1, band2) | |
| plv_std = float(np.std(sliding_plvs)) if len(sliding_plvs) > 0 else 0 | |
| plv_min = float(np.min(sliding_plvs)) if len(sliding_plvs) > 0 else 0 | |
| plv_max = float(np.max(sliding_plvs)) if len(sliding_plvs) > 0 else 0 | |
| # Classification | |
| if coherence_score >= 0.90: | |
| status = "HEALTHY" | |
| color = ACCENT_GREEN | |
| interpretation = "Strong theta-gamma phase coupling. Indicates healthy cross-frequency neural communication." | |
| elif coherence_score >= 0.70: | |
| status = "MODERATE" | |
| color = ACCENT_YELLOW | |
| interpretation = "Partial phase synchronization. May indicate subclinical disruption in cross-frequency dynamics." | |
| else: | |
| status = "LOW" | |
| color = ACCENT_RED | |
| interpretation = "Weak cross-frequency coupling. Consistent with disrupted neural coherence patterns." | |
| # Create figure | |
| fig = create_analysis_figure(t, eeg, fs, band1, band2, plv, coherence_score, | |
| status, color, sliding_times, sliding_plvs) | |
| # Get preset info | |
| preset_info = CLINICAL_PRESETS.get(preset, {}) | |
| # Build report | |
| report = f"""## GCFD Analysis Report | |
| ### Global Coherence Ratio: **{coherence_score:.4f}** ({status}) | |
| {interpretation} | |
| --- | |
| ### Phase Synchronization Metrics | |
| | Metric | Value | | |
| |--------|-------| | |
| | **Phase Locking Value (PLV)** | {plv:.4f} | | |
| | **Global Coherence Score** | {coherence_score:.4f} | | |
| | **PLV Temporal Stability (std)** | {plv_std:.4f} | | |
| | **PLV Range** | [{plv_min:.3f} — {plv_max:.3f}] | | |
| | **Band 1 Relative Power** | {band1_power:.4f} ({band1_power*100:.1f}%) | | |
| | **Band 2 Relative Power** | {band2_power:.4f} ({band2_power*100:.1f}%) | | |
| ### Signal Parameters | |
| | Parameter | Value | | |
| |-----------|-------| | |
| | Data Source | {data_source} | | |
| | Sampling Rate | {fs:.0f} Hz | | |
| | Duration | {t[-1]:.1f}s ({len(eeg):,} samples) | | |
| | Band 1 (Low Freq) | {band1[0]:.1f} — {band1[1]:.1f} Hz | | |
| | Band 2 (High Freq) | {band2[0]:.1f} — {band2[1]:.1f} Hz | | |
| | Sliding Window | 2.0s window, 0.5s step | | |
| """ | |
| if preset_info.get("citation"): | |
| report += f""" | |
| ### Clinical Reference | |
| **Preset**: {preset} | |
| > {preset_info.get('description', '')} | |
| **Expected Score Range**: {preset_info.get('expected_score', 'N/A')} | |
| **Citation**: {preset_info.get('citation', '')} | |
| """ | |
| report += """ | |
| --- | |
| ### Methodology | |
| **Phase Locking Value (PLV)** quantifies the consistency of the phase difference between two frequency-band-filtered signals over time. A PLV of 1.0 indicates perfect phase synchronization; 0.0 indicates no consistent phase relationship. | |
| **Pipeline**: Raw EEG → Butterworth bandpass (order 3, zero-phase) → Hilbert transform → instantaneous phase extraction → PLV computation → sliding window temporal analysis. | |
| **Global Coherence Score** maps PLV to a [0.5, 1.0] clinical scale where values above 0.90 indicate healthy cross-frequency coupling. | |
| **References**: | |
| - Lachaux et al. (1999) — *Measuring phase synchrony in brain signals*. Human Brain Mapping. | |
| - Canolty et al. (2006) — *High gamma power is phase-locked to theta oscillations*. Science. | |
| - Tort et al. (2010) — *Measuring phase-amplitude coupling*. J Neurophysiology. | |
| """ | |
| return fig, report | |
| def load_preset(preset_name): | |
| """Load clinical preset parameters.""" | |
| preset = CLINICAL_PRESETS.get(preset_name, CLINICAL_PRESETS["Custom (Manual)"]) | |
| return ( | |
| preset["theta_amp"], | |
| preset["gamma_amp"], | |
| preset["noise"], | |
| preset["seed"], | |
| preset.get("description", "") | |
| ) | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| # GRADIO APPLICATION | |
| # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| CSS = """ | |
| .gcfd-header { text-align: center; padding: 20px 0 10px 0; } | |
| .gcfd-header h1 { font-size: 2em; margin-bottom: 5px; } | |
| .metric-box { border: 1px solid #334155; border-radius: 8px; padding: 12px; background: #0f172a; } | |
| footer { display: none !important; } | |
| """ | |
| with gr.Blocks( | |
| title="GCFD Coherence Tracker | TAURUS AI Corp", | |
| theme=gr.themes.Base( | |
| primary_hue="blue", | |
| secondary_hue="purple", | |
| neutral_hue="slate", | |
| font=gr.themes.GoogleFont("Inter"), | |
| ), | |
| css=CSS, | |
| ) as demo: | |
| # ── Header ── | |
| gr.HTML(""" | |
| <div class="gcfd-header"> | |
| <h1>GCFD Coherence Tracker</h1> | |
| <p style="color: #94a3b8; font-size: 1.1em;"> | |
| Generalized Cross-Frequency Decomposition for EEG/MEG Phase Synchronization Analysis | |
| </p> | |
| <p style="color: #64748b; font-size: 0.85em;"> | |
| v1.0.0 | Built by <a href="https://taurusai.io" style="color: #38bdf8;">TAURUS AI Corp</a> | |
| | Global Bio-Foundry Initiative | |
| | Apache 2.0 License | |
| </p> | |
| </div> | |
| """) | |
| with gr.Tabs(): | |
| # ━━━━━━━ TAB 1: ANALYSIS ━━━━━━━ | |
| with gr.TabItem("Analysis"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Clinical Preset") | |
| preset = gr.Dropdown( | |
| choices=list(CLINICAL_PRESETS.keys()), | |
| value="Healthy Adult", | |
| label="Select Condition", | |
| info="Pre-configured signal parameters for common clinical states" | |
| ) | |
| preset_desc = gr.Textbox( | |
| value=CLINICAL_PRESETS["Healthy Adult"]["description"], | |
| label="Description", lines=2, interactive=False | |
| ) | |
| gr.Markdown("### Signal Parameters") | |
| duration = gr.Slider(1, 60, value=10, step=1, label="Duration (seconds)") | |
| fs = gr.Slider(100, 1000, value=250, step=50, label="Sampling Rate (Hz)") | |
| theta_amp = gr.Slider(0, 5, value=1.0, step=0.1, label="Theta Amplitude") | |
| gamma_amp = gr.Slider(0, 5, value=1.5, step=0.1, label="Gamma Amplitude") | |
| noise_level = gr.Slider(0, 5, value=0.8, step=0.1, label="Noise Level") | |
| seed = gr.Number(value=42, label="Random Seed", precision=0) | |
| gr.Markdown("### Frequency Bands") | |
| theta_low = gr.Slider(0.5, 12, value=4.0, step=0.5, label="Band 1 Low (Hz)") | |
| theta_high = gr.Slider(4, 15, value=8.0, step=0.5, label="Band 1 High (Hz)") | |
| gamma_low = gr.Slider(15, 80, value=30.0, step=1, label="Band 2 Low (Hz)") | |
| gamma_high = gr.Slider(30, 150, value=100.0, step=1, label="Band 2 High (Hz)") | |
| gr.Markdown("### Upload EEG Data (optional)") | |
| csv_upload = gr.File( | |
| label="CSV/TSV file (channels as columns, samples as rows)", | |
| file_types=[".csv", ".tsv", ".txt"], | |
| type="binary" | |
| ) | |
| analyze_btn = gr.Button("Run Coherence Analysis", variant="primary", size="lg") | |
| with gr.Column(scale=2): | |
| plot_output = gr.Plot(label="GCFD Analysis Dashboard") | |
| report_output = gr.Markdown() | |
| # Preset loader | |
| preset.change( | |
| fn=load_preset, | |
| inputs=[preset], | |
| outputs=[theta_amp, gamma_amp, noise_level, seed, preset_desc] | |
| ) | |
| # Analysis trigger | |
| analyze_btn.click( | |
| fn=run_analysis, | |
| inputs=[preset, duration, fs, theta_amp, gamma_amp, noise_level, seed, | |
| theta_low, theta_high, gamma_low, gamma_high, csv_upload], | |
| outputs=[plot_output, report_output] | |
| ) | |
| # ━━━━━━━ TAB 2: DOCUMENTATION ━━━━━━━ | |
| with gr.TabItem("Documentation"): | |
| gr.Markdown(""" | |
| ## GCFD Coherence Tracker — Technical Documentation | |
| ### What is GCFD? | |
| **Generalized Cross-Frequency Decomposition** is a method for quantifying the phase | |
| relationship between neural oscillations at different frequencies. The core metric | |
| is the **Phase Locking Value (PLV)**, which measures the consistency of the phase | |
| difference between two band-filtered signals. | |
| ### Why Cross-Frequency Coupling Matters | |
| Neural oscillations at different frequencies serve distinct computational roles: | |
| - **Theta (4-8 Hz)**: Memory encoding, spatial navigation, top-down control | |
| - **Gamma (30-100 Hz)**: Local cortical processing, feature binding, attention | |
| The **coupling** between theta and gamma (theta-gamma PAC/PLV) is a biomarker for: | |
| - Working memory capacity | |
| - Cognitive function in aging | |
| - Severity of neuropsychiatric conditions (MDD, ADHD, Alzheimer's) | |
| - Depth of anesthesia / consciousness level | |
| ### Signal Processing Pipeline | |
| ``` | |
| Raw EEG/MEG Signal | |
| | | |
| v | |
| [Butterworth Bandpass Filter] ─── Order 3, zero-phase (filtfilt) | |
| | Band 1: e.g., Theta (4-8 Hz) | |
| | Band 2: e.g., Gamma (30-100 Hz) | |
| v | |
| [Hilbert Transform] ─────────── Extract analytic signal | |
| | | |
| v | |
| [Phase Extraction] ──────────── np.angle(analytic_signal) | |
| | | |
| v | |
| [Phase Locking Value] ────────── PLV = |mean(exp(j * (phase1 - phase2)))| | |
| | | |
| v | |
| [Sliding Window Analysis] ───── 2s window, 0.5s step for temporal dynamics | |
| | | |
| v | |
| [Global Coherence Score] ────── Mapped to [0.5, 1.0] clinical scale | |
| ``` | |
| ### Clinical Interpretation Scale | |
| | Score Range | Classification | Interpretation | | |
| |-------------|---------------|----------------| | |
| | 0.90 - 1.00 | **HEALTHY** | Strong cross-frequency coupling | | |
| | 0.70 - 0.89 | **MODERATE** | Partial synchronization | | |
| | 0.50 - 0.69 | **LOW** | Weak coupling / potential pathology | | |
| **Note**: Scores above 0.95 may indicate pathological hypersynchronization (e.g., epileptic activity). | |
| ### CSV Upload Format | |
| Upload EEG data as CSV with: | |
| - **Rows**: Time samples | |
| - **Columns**: Channels (first column used if multiple) | |
| - **No time column** needed — timing derived from sampling rate parameter | |
| - **Units**: Microvolts (uV) preferred, but any consistent unit works | |
| ### Limitations | |
| - This tool uses **synthetic EEG** by default — clinical use requires real EEG data | |
| - PLV is sensitive to signal length and SNR — minimum 5 seconds recommended | |
| - Single-channel analysis only (multi-channel averaging planned for v2.0) | |
| - Phase-amplitude coupling (PAC) via Modulation Index not yet implemented | |
| ### References | |
| 1. Lachaux, J.P. et al. (1999). *Measuring phase synchrony in brain signals*. Human Brain Mapping, 8(4), 194-208. | |
| 2. Canolty, R.T. et al. (2006). *High gamma power is phase-locked to theta oscillations in human neocortex*. Science, 313(5793), 1626-1628. | |
| 3. Tort, A.B. et al. (2010). *Measuring phase-amplitude coupling between neuronal oscillations of different frequencies*. Journal of Neurophysiology, 104(2), 1195-1210. | |
| 4. Goodman, M.S. et al. (2018). *Theta-gamma coupling and working memory in Alzheimer's disease and mild cognitive impairment*. Frontiers in Aging Neuroscience, 10, 101. | |
| 5. Sun, L. et al. (2022). *Theta-gamma coupling deficit in MDD patients during working memory*. Brain Research Bulletin, 189, 49-57. | |
| """) | |
| # ━━━━━━━ TAB 3: API ━━━━━━━ | |
| with gr.TabItem("API"): | |
| gr.Markdown(""" | |
| ## API Access | |
| This Space exposes a REST API via Gradio's built-in endpoint system. | |
| ### Python Client | |
| ```python | |
| from gradio_client import Client | |
| client = Client("Taurus-Ai-Corp/gcfd-coherence-tracker") | |
| result = client.predict( | |
| preset="Healthy Adult", | |
| duration=10, | |
| fs=250, | |
| theta_amp=1.0, | |
| gamma_amp=1.5, | |
| noise_level=0.8, | |
| seed=42, | |
| theta_low=4.0, | |
| theta_high=8.0, | |
| gamma_low=30.0, | |
| gamma_high=100.0, | |
| csv_file=None, | |
| api_name="/run_analysis" | |
| ) | |
| ``` | |
| ### cURL | |
| ```bash | |
| curl -X POST https://Taurus-Ai-Corp-gcfd-coherence-tracker.hf.space/api/predict \\ | |
| -H "Content-Type: application/json" \\ | |
| -d '{"data": ["Healthy Adult", 10, 250, 1.0, 1.5, 0.8, 42, 4.0, 8.0, 30.0, 100.0, null]}' | |
| ``` | |
| ### Rate Limits | |
| | Tier | Rate | Access | | |
| |------|------|--------| | |
| | Free (this Space) | 50 requests/day | Open | | |
| | Researcher ($29/mo) | 1,000 requests/day | [Contact us](mailto:admin@taurusai.io) | | |
| | Clinical ($149/mo) | 10,000 requests/day + batch | [Contact us](mailto:admin@taurusai.io) | | |
| | Enterprise | Unlimited + SLA | [Contact us](mailto:admin@taurusai.io) | | |
| ### Upcoming Features (v2.0) | |
| - Multi-channel analysis with topographic maps | |
| - Phase-Amplitude Coupling (PAC) via Modulation Index | |
| - EDF/BDF file format support | |
| - Batch processing endpoint | |
| - Pre-trained EEG foundation model integration (braindecode/SignalJEPA) | |
| """) | |
| # ━━━━━━━ TAB 4: ABOUT ━━━━━━━ | |
| with gr.TabItem("About"): | |
| gr.Markdown(f""" | |
| ## About GCFD Coherence Tracker | |
| **Version**: {__version__} | |
| **Author**: [TAURUS AI Corp](https://taurusai.io) | |
| **License**: Apache License 2.0 | |
| **Repository**: [GitHub](https://github.com/Taurus-Ai-Corp) | |
| ### The Global Bio-Foundry Initiative | |
| TAURUS AI Corp's neuroscience research initiative explores the intersection of: | |
| - **Neural coherence** in biological systems | |
| - **Cross-frequency coupling** dynamics in EEG/MEG signals | |
| - **Open-source neuroscience** tooling for clinical research | |
| The GCFD Coherence Tracker is the open-source component of this initiative, | |
| providing standard DSP tools for EEG/MEG phase synchronization analysis. | |
| ### Citation | |
| If you use this tool in your research, please cite: | |
| ```bibtex | |
| @software{{gcfd_tracker_2026, | |
| title = {{GCFD Coherence Tracker: Cross-Frequency Phase Synchronization Analysis}}, | |
| author = {{TAURUS AI Corp}}, | |
| year = {{2026}}, | |
| version = {{{__version__}}}, | |
| url = {{https://huggingface.co/spaces/Taurus-Ai-Corp/gcfd-coherence-tracker}}, | |
| license = {{Apache-2.0}} | |
| }} | |
| ``` | |
| ### Related Work | |
| - [braindecode](https://braindecode.org/) — Deep learning for EEG/MEG | |
| - [MNE-Python](https://mne.tools/) — MEG/EEG analysis toolkit | |
| - [SignalJEPA](https://huggingface.co/braindecode/SignalJEPA-pretrained) — Brain signal foundation model | |
| ### Contact | |
| - Research inquiries: admin@taurusai.io | |
| - Enterprise licensing: admin@taurusai.io | |
| - Issues: [GitHub Issues](https://github.com/Taurus-Ai-Corp) | |
| """) | |
| # NOTE: Removed demo.load() auto-run to prevent startup crashes from | |
| # breaking the Gradio API. Users click "Run Coherence Analysis" to start. | |
| demo.launch() | |