Bioexplorer CZ Analysis Tool

import numpy as np

import pandas as pd

import csv

import os

import glob

# Load and convert data from the text file to prepare for analysis

file_path_pattern = '/mnt/data/*.epochs.txt' # Look for epoch files with wildcard

file_paths = glob.glob(file_path_pattern)

if len(file_paths) > 0:

file_path = file_paths[0] # Assuming we are working with the first matched file

# Convert the text file to CSV using similar logic as vocals_converter.py

with open(file_path) as csvfile:

reader = csv.DictReader(csvfile, delimiter='\t')

data = []

types = ['MEAN', 'MEANF', 'STDDEV', 'MODFRQ']

# Adjusted the column names to match those in the provided dataset

bands = {

'Delta': {'MEAN': 'Delta::Amplitude Mean', 'STDDEV': 'Delta::Amplitude Std Dev'},

'Theta': {'MEAN': 'Theta::Amplitude Mean', 'STDDEV': 'Theta::Amplitude Std Dev'},

'Alpha': {'MEAN': 'Alpha::Amplitude Mean', 'STDDEV': 'Alpha::Amplitude Std Dev'},

'Lo-Alpha': {'MEAN': 'Lo-Alpha::Amplitude Mean', 'STDDEV': 'Lo-Alpha::Amplitude Std Dev'},

'Hi-Alpha': {'MEAN': 'Hi-Alpha::Amplitude Mean', 'STDDEV': 'Hi-Alpha::Amplitude Std Dev'},

'SMR': {'MEAN': 'SMR::Amplitude Mean', 'STDDEV': 'SMR::Amplitude Std Dev'},

'Beta': {'MEAN': 'Beta::Amplitude Mean', 'STDDEV': 'Beta::Amplitude Std Dev'},

'Hi-Beta/Gamma': {'MEAN': 'Hi-Beta/Gamma::Amplitude Mean', 'STDDEV': 'Hi-Beta/Gamma::Amplitude Std Dev'}

}

for row in reader:

for type_ in types:

run = {'TYPE': str(type_)}

for band in bands:

try:

value = float(row.get(bands[band][type_], 'nan'))

if not np.isnan(value):

if value < 1:

run[band] = round(value * 1000000, 2)

else:

run[band] = round(value, 2)

except KeyError:

# Skip if the key is not found in the row

continue

except ValueError:

# Skip if the value cannot be converted to float (e.g., missing or non-numeric data)

continue

run['RUN'] = row.get('Run', None)

run['EPOCH'] = row['Time']

data.append(run)

# Write the converted data to a CSV file

converted_csv_path = '/mnt/data/converted_data.csv'

with open(converted_csv_path, 'w', newline='') as csvfile:

fieldnames = ['EPOCH', 'RUN', 'TYPE'] + list(bands.keys())

writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

writer.writeheader()

for row in data:

writer.writerow(row)

# Load the converted data into a DataFrame for analysis

data_df = pd.read_csv(converted_csv_path)

# Filter the data for amplitude mean values (for Clinical Q analysis)

data_filtered = data_df[data_df['TYPE'] == 'MEAN']

# Define fixed epochs and sites within the 4:40 (280 seconds) recording

epochs_mapping = {

'Cz': {'20s EO': (0, 20), '10s EC': (20, 30), '10s EO': (30, 40), '40s UT': (40, 80), '10s EO (after UT)': (80, 90)},

'O1': {'20s EO': (90, 110), '10s EC': (110, 120), '10s EO': (120, 130)},

'F3': {'30s EC': (130, 160)},

'F4': {'30s EC': (160, 190)},

'Fz': {'1:40 EC': (190, 280)}

}

# Define frequency bands of interest

bands_of_interest = ['Delta', 'Theta', 'Alpha', 'Lo-Alpha', 'Hi-Alpha', 'SMR', 'Beta', 'Hi-Beta/Gamma']

# Known detection vectors and their clinical implications

detection_vectors = {

'Delta': "High delta can indicate issues such as head trauma, learning disabilities, or excessive drowsiness.",

'Theta': "Elevated theta is often associated with ADHD, inattentiveness, or daydreaming.",

'Alpha': "Alpha activity is linked to relaxation and idling. Low alpha may indicate anxiety, while high alpha may relate to excessive relaxation or disengagement.",

'Lo-Alpha': "Low-alpha dominance can point to difficulty in focusing or an inability to relax.",

'Hi-Alpha': "High-alpha dominance can indicate a highly relaxed state, sometimes excessive in nature.",

'SMR': "Sensorimotor rhythm (SMR) is associated with calm focus. Low SMR may relate to impulsivity or hyperactivity.",

'Beta': "High beta is associated with active thinking and problem-solving. Excessive beta can indicate anxiety or hyperarousal.",

'Hi-Beta/Gamma': "High beta or gamma activity can indicate heightened stress, hypervigilance, or excessive cognitive processing."

}

# Generate a detailed report for Clinical Q/qEEG level analysis

report_lines = []

report_lines.append("==================== EEG Analysis Report ====================\n")

report_lines.append(f"File: {file_path}\n")

report_lines.append("\n==================== Channel-wise Analysis ====================\n")

# Process each site and its epochs to extract band power metrics and generate interpretations

protocol_guide = []

protocol_guide.append("==================== Suggested Protocol Guide ====================\n")

for site, epochs in epochs_mapping.items():

report_lines.append(f"\n-------------------------------------------------------------\n")

report_lines.append(f"Channel: {site}\n")

report_lines.append(f"-------------------------------------------------------------\n")

protocol_guide.append(f"\nChannel: {site}\n")

protocol_guide.append(f"-------------------------------------------------------------\n")

protocol_guide.append(" Suggested Protocol:\n")

for epoch_name, (start, end) in epochs.items():

report_lines.append(f" Epoch: {epoch_name}\n")

protocol_notes = []

# Calculate mean power for each band over the given epoch

for band in bands_of_interest:

try:

epoch_data = data_filtered.filter(like=band).iloc[start:end]

if epoch_data.isna().all().values[0]:

raise ValueError("No valid data available for this epoch")

mean_value = epoch_data.mean().values[0]

report_lines.append(f" {band} Mean Power: {mean_value:.8f}\n")

# Add clinical interpretation based on the detection vector

if band == 'Delta' and mean_value > 10.0:

report_lines.append(f" Alert: High Delta observed. {detection_vectors[band]}\n")

protocol_notes.append("Inhibit: Reduce Delta activity to address issues such as head trauma, learning disabilities, or excessive drowsiness.")

elif band == 'Theta' and mean_value > 12.0:

report_lines.append(f" Alert: Elevated Theta observed. {detection_vectors[band]}\n")

protocol_notes.append("Inhibit: Reduce Theta activity to mitigate inattention, daydreaming, and ADHD symptoms. Reward range: 4-8 Hz.")

elif band == 'Alpha' and mean_value < 8.0:

report_lines.append(f" Alert: Low Alpha detected. {detection_vectors[band]}\n")

protocol_notes.append("Reward: Increase Alpha activity to aid relaxation and reduce anxiety. Reward range: 8-12 Hz.")

elif band == 'SMR' and mean_value < 4.0:

report_lines.append(f" Alert: Low SMR observed. {detection_vectors[band]}\n")

protocol_notes.append("Reward: Increase SMR (12-15 Hz) to improve focus, relaxation, and calmness. Reward range: 12-15 Hz.")

elif band == 'Beta' and mean_value > 15.0:

report_lines.append(f" Alert: Elevated Beta observed. {detection_vectors[band]}\n")

protocol_notes.append("Inhibit: Reduce High Beta (22-30 Hz) to help manage anxiety and hypervigilance. Reward range: 15-20 Hz.")

except (IndexError, KeyError, ValueError):

report_lines.append(f" {band} Mean Power: Data not available for epoch range\n")

report_lines.append("\n")

if not protocol_notes:

protocol_notes.append("No specific abnormalities detected; use standard protocol adjustments based on symptom tracking.")

# Add the protocol notes for the current epoch

for note in protocol_notes:

protocol_guide.append(f" {note}\n")

protocol_guide.append(" Additional Notes: Tailor the training to the individual's response, adjusting based on symptom reduction and improvement in cognitive function.\n")

# Save the report and protocol guide to text files

report_file_path = '/mnt/data/eeg_analysis_report.txt'

protocol_file_path = '/mnt/data/eeg_protocol_guide.txt'

with open(report_file_path, 'w') as report_file:

report_file.writelines(report_lines)

with open(protocol_file_path, 'w') as protocol_file:

protocol_file.writelines(protocol_guide)

print(f"Report saved to {report_file_path}")

print(f"Protocol guide saved to {protocol_file_path}")