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| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from scipy import signal | |
| import pandas as pd | |
| # EEG + tACS simulation functions | |
| def generate_eeg_signal(duration=5, sampling_rate=250): | |
| """Generate simulated EEG signal with alpha waves""" | |
| t = np.linspace(0, duration, int(duration * sampling_rate), endpoint=False) | |
| alpha_wave = 0.5 * np.sin(2 * np.pi * 10 * t) # 10 Hz alpha rhythm | |
| noise = 0.2 * np.random.normal(size=len(t)) | |
| return t, alpha_wave + noise | |
| def apply_tacs(eeg_signal, tacs_freq, tacs_amp, tacs_phase, sampling_rate=250): | |
| """Apply tACS to EEG signal""" | |
| t = np.linspace(0, len(eeg_signal)/sampling_rate, len(eeg_signal)) | |
| tacs = tacs_amp * np.sin(2 * np.pi * tacs_freq * t + np.deg2rad(tacs_phase)) | |
| return eeg_signal + tacs, tacs | |
| def filter_signal(raw_signal, sampling_rate=250, lowcut=1, highcut=40): | |
| """Apply bandpass filter to EEG signal""" | |
| nyquist = 0.5 * sampling_rate | |
| low = lowcut / nyquist | |
| high = highcut / nyquist | |
| b, a = signal.butter(4, [low, high], btype='band') | |
| return signal.filtfilt(b, a, raw_signal) | |
| def plot_signals(t, eeg, tacs, combined, title): | |
| """Create matplotlib plot of signals""" | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| ax.plot(t, eeg, label='EEG Original', alpha=0.7) | |
| ax.plot(t, tacs, label='tACS Stimulation', alpha=0.7) | |
| ax.plot(t, combined, label='EEG + tACS', linewidth=1.5) | |
| ax.set_title(title, fontsize=14) | |
| ax.set_xlabel('Time (s)', fontsize=12) | |
| ax.set_ylabel('Amplitude (μV)', fontsize=12) | |
| ax.grid(True, linestyle='--', alpha=0.6) | |
| ax.legend() | |
| ax.set_xlim(0, 2) # Show first 2 seconds for clarity | |
| plt.tight_layout() | |
| return fig | |
| def simulate_tacs(tacs_frequency, tacs_amplitude, tacs_phase, show_fft): | |
| # Generate EEG signal | |
| t, eeg = generate_eeg_signal() | |
| # Apply tACS stimulation | |
| combined, tacs_wave = apply_tacs(eeg, tacs_frequency, tacs_amplitude, tacs_phase) | |
| # Filter the combined signal | |
| filtered = filter_signal(combined) | |
| # Create plots | |
| time_plot = plot_signals(t, eeg, tacs_wave, combined, | |
| f"EEG + tACS Simulation | {tacs_frequency}Hz, {tacs_amplitude}μA") | |
| # Optional FFT analysis | |
| if show_fft: | |
| fft_plot = plot_fft(combined, tacs_frequency) | |
| return time_plot, fft_plot, filtered[:250] # Return first second of filtered signal | |
| return time_plot, None, filtered[:250] | |
| def plot_fft(signal, stim_freq, fs=250): | |
| """Plot frequency spectrum of signal""" | |
| n = len(signal) | |
| freq = np.fft.rfftfreq(n, d=1/fs) | |
| mag = np.abs(np.fft.rfft(signal)) / n | |
| fig, ax = plt.subplots(figsize=(10, 4)) | |
| ax.plot(freq, mag) | |
| ax.axvline(x=stim_freq, color='r', linestyle='--', label=f'Stim Freq: {stim_freq}Hz') | |
| ax.set_title('Frequency Spectrum (FFT)', fontsize=14) | |
| ax.set_xlabel('Frequency (Hz)', fontsize=12) | |
| ax.set_ylabel('Magnitude', fontsize=12) | |
| ax.set_xlim(0, 50) | |
| ax.grid(True, linestyle='--', alpha=0.6) | |
| ax.legend() | |
| plt.tight_layout() | |
| return fig | |
| # Gradio application | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🧠 EEG + tACS Simulator") | |
| gr.Markdown("Simulieren Sie die Wirkung von transkranieller Wechselstromstimulation (tACS) auf EEG-Signale") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("## ⚡ Stimulationsparameter") | |
| tacs_freq = gr.Slider(1, 100, value=10, step=0.5, | |
| label="tACS Frequenz (Hz)", info="1-100 Hz") | |
| tacs_amp = gr.Slider(0.1, 5, value=1, step=0.1, | |
| label="Stromstärke (μA)", info="0.1-5 μA") | |
| tacs_phase = gr.Slider(0, 360, value=0, step=15, | |
| label="Phase (Grad)", info="0-360°") | |
| show_fft = gr.Checkbox(label="Frequenzspektrum anzeigen (FFT)") | |
| btn = gr.Button("Simulation starten", variant="primary") | |
| gr.Markdown("### ℹ️ Über tACS") | |
| gr.Markdown(""" | |
| **Transkranielle Wechselstromstimulation (tACS)** ist eine nicht-invasive Methode zur Modulation neuronaler Aktivität: | |
| - Beeinflusst die Gehirnrhythmen durch externe Wechselströme | |
| - Typische Frequenzen: Alpha (8-12 Hz), Beta (12-30 Hz), Gamma (30-100 Hz) | |
| - Anwendungen: Kognitionsverbesserung, Stimmungsregulation, Schmerztherapie | |
| """) | |
| with gr.Column(scale=2): | |
| gr.Markdown("## 📊 Simulationsergebnisse") | |
| time_plot = gr.Plot(label="Zeitbereich") | |
| fft_plot = gr.Plot(label="Frequenzbereich", visible=False) | |
| signal_output = gr.Dataframe( | |
| label="Gefiltertes Signal (erste Sekunde)", | |
| headers=["Zeitindex", "Amplitude (μV)"], | |
| datatype=["number", "number"], | |
| max_rows=10 | |
| ) | |
| gr.Markdown("### 💡 Interpretation") | |
| gr.Markdown(""" | |
| - **Rote Linie**: tACS Stimulationsfrequenz | |
| - **Blaue Linie**: Natürliche EEG-Aktivität (Alpha-Wellen ~10Hz) | |
| - **Grüne Linie**: Kombinierter Effekt von EEG + tACS | |
| """) | |
| # Built with anycoder | |
| gr.Markdown( | |
| "[Built with anycoder](https://huggingface.co/spaces/akhaliq/anycoder)", | |
| elem_classes=["footer"] | |
| ) | |
| # Event handling | |
| btn.click( | |
| simulate_tacs, | |
| inputs=[tacs_freq, tacs_amp, tacs_phase, show_fft], | |
| outputs=[time_plot, fft_plot, signal_output] | |
| ) | |
| show_fft.change( | |
| lambda x: gr.update(visible=x), | |
| inputs=show_fft, | |
| outputs=fft_plot | |
| ) | |
| # Launch with modern theme | |
| demo.launch( | |
| theme=gr.themes.Soft( | |
| primary_hue="blue", | |
| secondary_hue="indigo", | |
| font=[gr.themes.GoogleFont("Montserrat"), "sans-serif"] | |
| ), | |
| css=".footer {text-align: center; margin-top: 1em; font-size: 0.8em}", | |
| footer_links=[{"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}] | |
| ) |