temp fc

fc.py

@@ -0,0 +1,207 @@
+import mne
+import numpy as np
+
+from mne.preprocessing.nirs import optical_density, beer_lambert_law
+from mne_connectivity import spectral_connectivity_epochs
+from mne_connectivity.viz import plot_connectivity_circle
+
+raw = mne.io.read_raw_snirf("E:/CVI_V_Adults_Cor/P21_CVI_V_updated.snirf", preload=True)
+
+raw.info["bads"] = []  # mark bad channels here if needed
+
+raw_od = optical_density(raw)
+raw_hb = beer_lambert_law(raw_od)
+
+raw_hbo = raw_hb.copy().pick(picks="hbo")
+
+raw_hbo.filter(
+    l_freq=0.01,
+    h_freq=0.2,
+    picks="hbo",
+    verbose=False
+)
+
+events = mne.make_fixed_length_events(
+    raw_hbo,
+    duration=30.0
+)
+
+epochs = mne.Epochs(
+    raw_hbo,
+    events,
+    tmin=0,
+    tmax=30.0,
+    baseline=None,
+    preload=True,
+    verbose=False
+)
+
+data = epochs.get_data()        # (n_epochs, n_channels, n_times)
+names = epochs.ch_names
+sfreq = epochs.info["sfreq"]
+
+
+con = spectral_connectivity_epochs(
+    data,
+    method=["coh", "plv"],
+    mode="multitaper",
+    sfreq=sfreq,
+    fmin=0.04,
+    fmax=0.2,
+    faverage=True,
+    verbose=True
+)
+
+
+con_coh, con_plv = con
+
+
+coh = con_coh.get_data(output="dense").squeeze()
+plv = con_plv.get_data(output="dense").squeeze()
+
+np.fill_diagonal(coh, 0)
+np.fill_diagonal(plv, 0)
+
+plot_connectivity_circle(
+    coh,
+    names,
+    title="fNIRS Functional Connectivity (HbO - Coherence)",
+    n_lines=40
+)
+
+
+
+from mne_connectivity import envelope_correlation
+env = envelope_correlation(
+    data,
+    orthogonalize=False,
+    absolute=True
+)
+env_data = env.get_data(output="dense")
+
+env_corr = env_data.mean(axis=0)
+
+env_corr = np.squeeze(env_corr)
+
+np.fill_diagonal(env_corr, 0)
+
+plot_connectivity_circle(
+    env_corr,
+    epochs.ch_names,
+    title="fNIRS HbO Envelope Correlation (Task Connectivity)",
+    n_lines=40
+)
+
+
+from mne_nirs.statistics import run_glm
+from mne_nirs.experimental_design import make_first_level_design_matrix
+
+raw_hb.annotations.description = [
+    f"Reach_{i}" if d == "Reach" else d 
+    for i, d in enumerate(raw_hb.annotations.description)
+]
+
+design_matrix = make_first_level_design_matrix(
+    raw_hb, 
+    stim_dur=1.0,      # We assume a short burst since duration is unknown
+    hrf_model='fir',   # Finite Impulse Response
+    fir_delays=np.arange(0, 12) # Look at 0-20 seconds after onset
+)
+
+# 2. Run the GLM 
+# This calculates the brain's response for every channel
+glm_est = run_glm(raw_hb, design_matrix)
+
+import pandas as pd
+
+# 3. Extract Beta Weights
+beta_df = glm_est.to_dataframe()
+
+print("\n--- DEBUG: Dataframe Info ---")
+print(f"Total rows in beta_df: {len(beta_df)}")
+print(f"Columns available: {list(beta_df.columns)}")
+print(f"Unique Chroma values: {beta_df['Chroma'].unique()}")
+print(f"First 5 unique Conditions: {beta_df['Condition'].unique()[:5]}")
+
+# FIX: Use .str.contains() because FIR conditions are named like 'Reach[5.0]'
+# We filter for HbO AND any condition that starts with 'Reach'
+hbo_betas = beta_df[(beta_df['Chroma'] == 'hbo') & 
+                    (beta_df['Condition'].str.contains('Reach'))]
+
+hbo_betas = hbo_betas.copy()
+hbo_betas[['Trial', 'Delay']] = hbo_betas['Condition'].str.extract(r'(Reach_\d+)_delay_(\d+)')
+
+# 2. Find which DELAY (time point) is best across ALL trials
+# We convert 'Delay' to numeric so we can sort them properly later if needed
+hbo_betas['Delay'] = pd.to_numeric(hbo_betas['Delay'])
+
+# IMPORTANT: We ignore delay 0 and 1 because they are usually 0.0000 (stimulus onset)
+# Brain responses in fNIRS usually peak between delays 4 and 8 (4-8 seconds)
+
+mask = (hbo_betas['Delay'] >= 4) & (hbo_betas['Delay'] <= 8)
+hbo_window = hbo_betas[mask]
+
+if hbo_window.empty:
+    print("Warning: No data found in the 4-8s window. Check your 'fir_delays' range.")
+    # Fallback to whatever is available if 4-8 is missing
+    mean_by_delay = hbo_betas.groupby('Delay')['theta'].mean()
+else:
+    mean_by_delay = hbo_window.groupby('Delay')['theta'].mean()
+
+peak_delay_num = mean_by_delay.idxmax()
+
+print(f"\n--- DEBUG: FIR Timing ---")
+print(f"Delays analyzed: {list(mean_by_delay.index)}")
+print(f"Peak brain response found at delay: {peak_delay_num}")
+
+# 3. Filter the data to ONLY include that peak delay across ALL trials
+peak_df = hbo_betas[hbo_betas['Delay'] == peak_delay_num]
+
+mne_order = raw_hbo.ch_names
+
+# 4. Pivot: Rows = Trials (Reach_1, Reach_2...), Columns = Channels
+beta_pivot = peak_df.pivot(index='Trial', columns='ch_name', values='theta')
+
+beta_pivot = beta_pivot.reindex(columns=mne_order)
+
+print(f"Pivot table shape: {beta_pivot.shape} (Should be something like 30 trials x 26 channels)")
+
+# 5. Correlation (Now it has a series of data to correlate!)
+beta_corr_matrix = beta_pivot.corr().values
+np.fill_diagonal(beta_corr_matrix, 0)
+
+# Replace any NaNs with 0 (occurs if a channel has 0 variance)
+beta_corr_matrix = np.nan_to_num(beta_corr_matrix)
+
+import matplotlib.pyplot as plt
+channel_names = beta_pivot.columns.tolist()
+# Create the plot
+plot_connectivity_circle(
+    beta_corr_matrix, 
+    channel_names,
+    n_lines=40,               # Show only the top 40 strongest connections
+    title=f"FIR Beta Series Connectivity)",
+)
+
+# 1. Aggregate the mean response for each delay across all trials and channels
+# We want to see the general 'shape' of the Reach response
+time_points = np.arange(0, 12)  # Matches your fir_delays
+average_response = hbo_betas.groupby('Delay')['theta'].mean()
+
+# 2. Plotting
+plt.figure(figsize=(10, 6))
+for ch in hbo_betas['ch_name'].unique():
+    ch_data = hbo_betas[hbo_betas['ch_name'] == ch].groupby('Delay')['theta'].mean()
+    plt.plot(time_points, ch_data, color='gray', alpha=0.3) # Individual channels
+
+# Plot the 'Grand Average' in bold red
+plt.plot(time_points, average_response, color='red', linewidth=3, label='Grand Average')
+
+plt.axvline(x=4, color='green', linestyle='--', label='Window Start (4s)')
+plt.axvline(x=8, color='green', linestyle='--', label='Window End (8s)')
+plt.title("FIR Hemodynamic Response to 'Reach' (HbO)")
+plt.xlabel("Seconds after Stimulus")
+plt.ylabel("HbO Concentration (Beta Weight)")
+plt.legend()
+plt.grid(True, alpha=0.3)
+plt.show()