yousif-raiyan-pip-package 1.8.6
Comprehensive EEG analysis pipeline with advanced spectral analysis, connectivity analysis, memory-safe HDF5 graph processing, trigger detection, and spectral parameterization (FOOOF/SpecParam)
Requires Python: >=3.7
Keywords: eeg, signal-processing, neuroscience, spectral-analysis, connectivity-analysis, graph-analysis, coherence, correlation, fooof, specparam, spectral-parameterization, aperiodic, oscillations, hdf5, memory-safe, large-files, multi-band, frequency-bands

# EEG Analysis Pipeline

A comprehensive Python package for EEG signal processing, trigger detection, and frequency-domain analysis.

## Overview

This package provides a complete pipeline for analyzing EEG data stored in European Data Format (EDF) files. It includes tools for signal loading, trigger detection, inter-trigger window analysis, and multi-band frequency-domain processing.

## Features

- **EDF File Loading**: Load and inspect EEG signals with flexible duration and channel selection
- **Trigger Detection**: Automated detection of trigger events with customizable thresholds
- **Window Analysis**: Generate and analyze inter-trigger intervals with multiple aggregation methods
- **Advanced Spectral Analysis**: Multi-band power analysis with temporal smoothing and spectral parameterization
- **Connectivity Analysis**: Graph-based network analysis with correlation, coherence, and phase metrics
- **Traditional Analysis**: Multi-band EEG analysis (Delta, Theta, Alpha, Beta, Gamma) 
- **Spectral Parameterization**: Separate aperiodic (1/f) and periodic (oscillatory) components using SpecParam/FOOOF
- **Memory-Safe Processing**: HDF5-based analysis for large EEG files with bounded memory usage
- **Professional Organization**: Structured output directories with consistent naming conventions
- **Analysis Metadata**: Complete tracking of analysis parameters, timing, and results
- **Professional Visualizations**: Publication-ready plots and comprehensive analysis reports
- **ML Integration**: Optional machine learning-based window quality filtering

## Installation

```bash
pip install yousif-raiyan-pip-package
```

## Quick Start

```python
from yousif_raiyan_pip_package import EDFLoader, TriggerDetector, Analyzer, SpectralAnalyzer, ConnectivityAnalyzer, FOOOFAnalyzer

# Load EEG data
loader = EDFLoader("data", "subject_name")
loader.load_and_plot_signals(signal_indices=[15, 25], duration=1200.0)  # T6, T2

# Detect triggers for temporal segmentation
detector = TriggerDetector(loader, 'T2')
detector.detect_triggers()

# Option 1: Traditional spectral analysis
analyzer = Analyzer(loader, detector)
analyzer.extract_signals()

# Option 2: Advanced spectral analysis (NEW)
spectral_analyzer = SpectralAnalyzer(loader=loader, trigger_detector=detector)
spectral_analyzer.analyze_comprehensive()  # Multi-band + spectral parameterization

# Option 3: Spectral parameterization only (SpecParam/FOOOF)
fooof_analyzer = FOOOFAnalyzer(loader)
fooof_analyzer.analyze_signals()

# Option 4: Graph-based connectivity analysis (NEW)
connectivity_analyzer = ConnectivityAnalyzer(edf_loader=loader, window_step_ratio=1.0)

# Level 1: Quick exploration
connectivity_analyzer.compute_correlation(start_time=0, stop_time=300, interval_seconds=30)
connectivity_analyzer.compute_coherence_average(start_time=0, stop_time=300, interval_seconds=30)

# Level 2: Detailed analysis  
connectivity_analyzer.compute_coherence_bands(start_time=0, stop_time=300, interval_seconds=10)
connectivity_analyzer.plot_connectivity_matrices()

# Level 3: Full graph representations
# Memory-safe graph generation (works for any file size):
hdf5_path = connectivity_analyzer.generate_graphs(segment_duration=60.0, overlap_ratio=0.875)
```

## Data Structure Requirements

### Input Data Format

Your EDF files should be organized as follows:

```
data/
└── subject_name/
    └── subject_name.edf
```

**Example:**
```
data/
└── Sebastian/
    └── Sebastian.edf
```

### EDF File Requirements

- **Format**: European Data Format (.edf)
- **Channels**: Standard EEG channel names (Fp1, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz)
- **Sample Rate**: Typically 500 Hz (automatically detected)
- **Duration**: Minimum 10 minutes recommended for trigger detection

## Classes and Methods

### EDFLoader

Handles loading and inspection of EDF files.

#### Initialization
```python
loader = EDFLoader(folder_path, name)
```

**Parameters:**
- `folder_path` (str): Base directory containing subject folders
- `name` (str): Subject name (must match folder and file name)

#### Methods

##### `inspect_data()`
Displays comprehensive file information including:
- File header details
- Number of signals and their properties
- Sample rates and signal ranges
- First 10 samples of each channel

```python
loader.inspect_data()
```

##### `load_and_plot_signals(signal_indices=None, duration=None, save_plots=False, save_path=None)`
Loads and visualizes EEG signals with flexible options.

**Parameters:**
- `signal_indices` (list, optional): Specific channel indices to load (default: all channels)
- `duration` (float, optional): Duration in seconds to load (default: entire file)
- `save_plots` (bool): Save plots instead of displaying (default: False)
- `save_path` (str, optional): Custom save directory (default: `plots/{subject_name}`)

**Examples:**
```python
# Load T6 and T2 channels for 20 minutes
loader.load_and_plot_signals(signal_indices=[15, 25], duration=1200.0)

# Load all channels and save plots
loader.load_and_plot_signals(save_plots=True)

# Load specific duration with custom save path
loader.load_and_plot_signals(duration=1200.0, save_plots=True, save_path="custom_plots")
```

**Output:**
- Time-series plots with time axis in seconds
- Saved to `plots/{subject_name}/signals_plot.png` (if save_plots=True)

### TriggerDetector

Detects triggers and analyzes inter-trigger windows.

#### Initialization
```python
detector = TriggerDetector(edf_loader, signal_choice)
```

**Parameters:**
- `edf_loader` (EDFLoader): Initialized EDFLoader instance
- `signal_choice` (str): Channel name for trigger detection (e.g., 'T2', 'O1')

#### Methods

##### `detect_triggers()`
Detects trigger events using amplitude thresholding.

**Algorithm:**
1. Rectifies and filters the signal (Butterworth low-pass, 30 Hz cutoff)
2. Applies amplitude threshold (60 µV)
3. Filters events by duration (52-65 seconds)

```python
detector.detect_triggers()
print(f"Found {len(detector.df_triggers)} triggers")
```

**Output:**
- `df_triggers` DataFrame with columns:
  - `start_index`, `end_index`: Sample indices
  - `start_time (s)`, `end_time (s)`: Time in seconds
  - `duration_time (s)`: Trigger duration

##### `plot_triggers()`
Visualizes detected triggers overlaid on the filtered signal.

```python
detector.plot_triggers()
```

##### `save_triggers()`
Saves trigger information to CSV file.

```python
detector.save_triggers()
```

**Output:** `{subject_folder}/triggers.csv`

##### `plot_windows()`
Generates individual plots for each inter-trigger window.

```python
detector.plot_windows()
```

**Output:** `{subject_folder}/window plots/plot_{i}.png`

##### `convert_to_video()`
Creates MP4 video from window plots for rapid review.

```python
detector.convert_to_video()
```

**Output:** `{subject_folder}/trigger.mp4`

##### `filter_bad_windows(clf_path=None, classes_path=None)`
ML-based filtering of poor-quality windows using ResNet-50 + logistic regression.

```python
# Use built-in models (recommended)
detector.filter_bad_windows()

# Or use custom models
detector.filter_bad_windows(
    clf_path="path/to/custom_classifier.pkl",
    classes_path="path/to/custom_classes.npy"
)
```

**Parameters:**
- `clf_path` (str, optional): Path to custom classifier (.pkl file). Uses built-in model if None.
- `classes_path` (str, optional): Path to custom class labels (.npy file). Uses built-in model if None.

### Analyzer

Performs frequency-domain analysis of inter-trigger windows.

#### Initialization
```python
analyzer = Analyzer(loader, trigger_detector, target_length=50)
```

**Parameters:**
- `loader` (EDFLoader): Initialized EDFLoader instance
- `trigger_detector` (TriggerDetector): Initialized TriggerDetector instance
- `target_length` (int): Resampled points per segment for aggregation

#### Methods

##### `plot_signal_window(window_index, lead)`
Plots raw signal for a specific inter-trigger window.

```python
analyzer.plot_signal_window(window_index=0, lead='T2')
```

##### `plot_average_window(channel, start_window=None, end_window=None, target_length=500, aggregation_method='mean', trim_ratio=0.1)`
Aggregates and plots multiple windows using various statistical methods.

**Parameters:**
- `channel` (str): Channel name to analyze
- `start_window`, `end_window` (int, optional): Window range
- `target_length` (int): Resampling length
- `aggregation_method` (str): 'mean', 'median', or 'trimmed'
- `trim_ratio` (float): Trimming ratio for 'trimmed' method

**Examples:**
```python
# Mean aggregation
analyzer.plot_average_window('T6', aggregation_method='mean')

# Robust median aggregation
analyzer.plot_average_window('T6', aggregation_method='median')

# Trimmed mean (removes 10% outliers)
analyzer.plot_average_window('T6', aggregation_method='trimmed', trim_ratio=0.1)
```

##### `extract_signals(channels_to_extract=None)`
Comprehensive frequency-domain analysis across all EEG bands.

**Parameters:**
- `channels_to_extract` (list, optional): Specific channels to process (default: all loaded)

**Processing Pipeline:**
1. **Band-pass filtering** for each EEG band:
   - Delta (0.5-4 Hz)
   - Theta (4-8 Hz)  
   - Alpha (8-12 Hz)
   - Beta (12-30 Hz)
   - Gamma (30-80 Hz)

2. **Signal rectification** (absolute value for power estimation)

3. **Moving-average smoothing** with multiple window sizes:
   - 100 ms, 250 ms, 500 ms

4. **Median aggregation** across all windows for robustness

**Examples:**
```python
# Process all loaded channels
analyzer.extract_signals()

# Process specific channels only
analyzer.extract_signals(['T2', 'T6', 'O1'])
```

### FOOOFAnalyzer

Performs spectral parameterization using SpecParam (formerly FOOOF - Fitting Oscillations & One Over F) to separate neural power spectra into aperiodic (1/f background) and periodic (oscillatory peaks) components. This provides insights into the underlying neural dynamics beyond traditional frequency band analysis. The analyzer automatically uses the modern SpecParam library with FOOOF fallback for compatibility.

#### Initialization
```python
fooof_analyzer = FOOOFAnalyzer(edf_loader)
```

**Parameters:**
- `edf_loader` (EDFLoader): Initialized EDFLoader instance with loaded signals

#### Methods

##### `set_frequency_range(freq_range)`
Set the frequency range for FOOOF analysis.

```python
fooof_analyzer.set_frequency_range((1, 50))  # Analyze 1-50 Hz
```

##### `set_fooof_settings(**kwargs)`
Customize FOOOF model parameters.

```python
fooof_analyzer.set_fooof_settings(
    max_n_peaks=8,              # Maximum number of peaks to fit
    peak_threshold=1.5,         # Peak detection threshold
    aperiodic_mode='knee',      # 'fixed' or 'knee' for aperiodic fit
    peak_width_limits=(1, 12)   # Peak width constraints
)
```

##### `analyze_signals(channels_to_analyze=None)`
Run FOOOF analysis on specified channels and save comprehensive results.

**Parameters:**
- `channels_to_analyze` (list, optional): Specific channels to process (default: all loaded)

**Processing Pipeline:**
1. **Signal preprocessing**: Detrending, bandpass filtering, notch filtering
2. **Power spectral density**: Welch's method with configurable parameters
3. **FOOOF fitting**: Separates aperiodic and periodic components
4. **Band power calculation**: Traditional frequency band powers
5. **Comprehensive output**: Individual and summary results

```python
# Analyze all loaded channels
fooof_analyzer.analyze_signals()

# Analyze specific channels
fooof_analyzer.analyze_signals(['T2', 'T6', 'O1'])
```

**Output Structure:**
```
data/subject_name/
└── fooof_analysis/
    ├── fooof_summary.csv              # Summary across all channels
    ├── band_powers_summary.csv        # Band powers across all channels
    ├── aperiodic_exponent_summary.png # Aperiodic exponent comparison
    ├── n_peaks_summary.png            # Peak count comparison
    ├── band_powers_heatmap.png        # Band power heatmap
    └── T2/                            # Per-channel results
        ├── T2_fooof_fit.png           # Spectral parameterization model fit
        ├── T2_psd.png                 # Power spectral density
        ├── T2_fooof_params.csv        # Spectral parameterization parameters
        ├── T2_band_powers.csv         # Traditional band powers
        └── T2_fooof_settings.json     # Analysis settings
```

##### `plot_channel_comparison(channels=None, metric='aperiodic_exponent')`
Compare FOOOF metrics across channels.

**Parameters:**
- `channels` (list, optional): Channels to compare (default: all analyzed)
- `metric` (str): Metric to compare - 'aperiodic_exponent', 'aperiodic_offset', 'n_peaks', 'r_squared', 'error'

```python
# Compare aperiodic exponents (1/f slope)
fooof_analyzer.plot_channel_comparison(metric='aperiodic_exponent')

# Compare number of detected peaks
fooof_analyzer.plot_channel_comparison(metric='n_peaks')

# Compare model fit quality
fooof_analyzer.plot_channel_comparison(metric='r_squared')
```

#### Spectral Parameterization Analysis Workflow

```python
from yousif_raiyan_pip_package import EDFLoader, FOOOFAnalyzer

# Load EEG data
loader = EDFLoader("data", "subject_name")
loader.load_and_plot_signals(duration=1200.0)

# Initialize spectral parameterization analyzer
fooof_analyzer = FOOOFAnalyzer(loader)

# Optional: Customize analysis parameters
fooof_analyzer.set_frequency_range((1, 50))
fooof_analyzer.set_fooof_settings(
    max_n_peaks=8,
    peak_threshold=1.5,
    aperiodic_mode='knee'
)

# Run analysis
fooof_analyzer.analyze_signals()

# View comparisons
fooof_analyzer.plot_channel_comparison(metric='aperiodic_exponent')
fooof_analyzer.plot_channel_comparison(metric='n_peaks')

# Access results programmatically
for channel, result in fooof_analyzer.results.items():
    print(f"Channel {channel}:")
    print(f"  Aperiodic exponent: {result['aperiodic_params'][1]:.3f}")
    print(f"  Number of peaks: {len(result['peak_params'])}")
    if len(result['peak_params']) > 0:
        dominant_peak = result['peak_params'][np.argmax(result['peak_params'][:, 1])]
        print(f"  Dominant peak: {dominant_peak[0]:.1f} Hz")
```

#### Spectral Parameterization Results Interpretation

**Aperiodic Parameters:**
- **Offset**: Overall power level (y-intercept of 1/f fit)
- **Exponent**: 1/f slope (higher = steeper decay, more "pink" noise)

**Peak Parameters (per detected peak):**
- **Center Frequency**: Peak frequency in Hz
- **Power**: Peak height above aperiodic background
- **Bandwidth**: Full-width at half-maximum of the peak

**Quality Metrics:**
- **R-squared**: Proportion of variance explained by the model
- **Error**: Mean absolute error between model and data

**Clinical Relevance:**
- **Aperiodic exponent**: Reflects E/I balance, aging, cognitive state
- **Peak parameters**: Identify dominant rhythms (alpha, beta, etc.)
- **Band powers**: Traditional frequency analysis for comparison

### SpectralAnalyzer

Advanced spectral analysis tool providing comprehensive frequency-domain characterization of EEG signals with both time-domain power analysis and modern spectral parameterization methods. Features professional output organization and complete analysis metadata tracking.

The SpectralAnalyzer offers **two complementary analysis approaches**:

1. **Multi-band Power Analysis** - Time-resolved power across canonical EEG frequency bands
2. **Spectral Parameterization** - FOOOF/SpecParam analysis separating aperiodic and periodic components

#### Initialization
```python
from yousif_raiyan_pip_package import EDFLoader, TriggerDetector, SpectralAnalyzer

# Load EEG data and detect triggers
loader = EDFLoader(folder_path="data", name="subject_name")
loader.load_and_plot_signals()

trigger_detector = TriggerDetector(edf_loader=loader, signal_choice='T2')
trigger_detector.detect_triggers()

# Initialize analyzer with optional custom output directory
analyzer = SpectralAnalyzer(
    loader=loader, 
    trigger_detector=trigger_detector, 
    target_length=50,
    output_dir=None  # Optional: defaults to subject_folder/spectral_analysis_results/
)
```

**Parameters:**
- `loader` (EDFLoader): Loaded EEG data instance
- `trigger_detector` (TriggerDetector): For temporal segmentation (required for multi-band analysis)
- `target_length` (int): Resampled points per segment for temporal aggregation
- `output_dir` (str, optional): Custom output directory path

#### Methods

##### `analyze_multiband_power(channels_to_analyze=None)`

Executes comprehensive multi-band power analysis across canonical EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) with configurable temporal smoothing.

**Features:**
- Butterworth bandpass filtering for frequency band isolation
- Signal rectification and moving-average smoothing
- Multiple smoothing windows (100ms, 250ms, 500ms) for different temporal scales
- Structured CSV output and publication-ready visualizations

```python
# Analyze all loaded channels
analyzer.analyze_multiband_power()

# Analyze specific channels
analyzer.analyze_multiband_power(channels_to_analyze=['T2', 'O1', 'F3'])
```

**Output Structure:**
```
subject_folder/
└── spectral_analysis_results/
    ├── multiband_power/
    │   ├── csv/
    │   │   ├── subject_multiband_Delta_ma100ms.csv
    │   │   ├── subject_multiband_Theta_ma250ms.csv
    │   │   └── subject_multiband_Gamma_ma500ms.csv
    │   └── plots/
    │       ├── subject_multiband_Delta_T2.png
    │       └── subject_multiband_Theta_T2.png
    └── analysis_metadata.json  # Complete analysis tracking
```

##### `analyze_spectral_parameterization(channels_to_analyze=None)`

Executes advanced spectral parameterization using FOOOF/SpecParam methodology to separate neural power spectra into aperiodic (1/f) and periodic (oscillatory) components.

**Features:**
- Automatic library detection (SpecParam preferred, FOOOF fallback)
- Robust model fitting with configurable parameters
- Comprehensive validation metrics and goodness-of-fit assessment
- Frequency band power quantification with aperiodic correction

```python
# Analyze all loaded channels
analyzer.analyze_spectral_parameterization()

# Analyze specific channels
analyzer.analyze_spectral_parameterization(channels_to_analyze=['T2', 'O1'])
```

**Output Structure:**
```
subject_folder/
└── spectral_analysis_results/
    ├── spectral_parameterization/
    │   ├── individual/
    │   │   ├── subject_fooof_T2.png
    │   │   ├── subject_fooof_parameters_T2.csv
    │   │   └── subject_band_powers_T2.csv
    │   ├── summary/
    │   │   ├── subject_fooof_parameters_summary.csv
    │   │   └── subject_band_powers_summary.csv
    │   └── plots/
    │       ├── subject_aperiodic_exponent_comparison.png
    │       └── subject_spectral_peaks_comparison.png
    └── analysis_metadata.json  # Complete analysis tracking
```

##### `analyze_comprehensive(channels_to_analyze=None)`

Executes complete spectral analysis suite combining both multi-band power analysis and spectral parameterization for comprehensive frequency-domain characterization.

```python
# Complete analysis workflow
analyzer.analyze_comprehensive(channels_to_analyze=['T2', 'O1', 'F3'])
```

#### Configuration Methods

##### `set_frequency_bands(bands_dict)`

Configure custom frequency bands for multi-band analysis.

```python
# Custom frequency bands
custom_bands = {
    'slow_alpha': (8, 10),
    'fast_alpha': (10, 12),
    'low_beta': (12, 20),
    'high_beta': (20, 30)
}
analyzer.set_frequency_bands(custom_bands)
```

##### `set_fooof_parameters(freq_range=None, **fooof_kwargs)`

Configure spectral parameterization parameters.

```python
# Custom FOOOF settings
analyzer.set_fooof_parameters(
    freq_range=(1, 40),
    peak_width_limits=(1, 8),
    max_n_peaks=6,
    min_peak_height=0.1
)
```

##### `set_smoothing_windows(window_secs_list)`

Configure temporal smoothing parameters for multi-band analysis.

```python
# Custom smoothing windows
analyzer.set_smoothing_windows([0.05, 0.1, 0.25])  # 50ms, 100ms, 250ms
```

#### Visualization Methods

##### `plot_raw_signal_window(window_index, channel)`

Generate publication-ready visualization of raw EEG data for specified trigger-defined window.

```python
# Plot specific window
analyzer.plot_raw_signal_window(window_index=5, channel='T2')
```

##### `plot_averaged_signal_window(channel, start_window=None, end_window=None, target_length=500, aggregation_method='mean')`

Create ensemble-averaged signal visualization across multiple temporal windows with robust statistical aggregation.

```python
# Plot averaged signal across windows 10-20
analyzer.plot_averaged_signal_window(
    channel='T2',
    start_window=10,
    end_window=20,
    aggregation_method='median'
)
```

##### `plot_fooof_comparison(channels=None, metric='aperiodic_exponent')`

Generate comparative visualization of spectral parameterization metrics across channels.

```python
# Compare aperiodic exponents across channels
analyzer.plot_fooof_comparison(
    channels=['T2', 'O1', 'F3'],
    metric='aperiodic_exponent'
)
```

#### Complete Analysis Example

```python
from yousif_raiyan_pip_package import EDFLoader, TriggerDetector, SpectralAnalyzer

# Step 1: Load data and detect triggers
loader = EDFLoader(folder_path="data", name="subject_name")
loader.load_and_plot_signals()

trigger_detector = TriggerDetector(edf_loader=loader, signal_choice='T2')
trigger_detector.detect_triggers()

# Step 2: Initialize analyzer
analyzer = SpectralAnalyzer(
    loader=loader,
    trigger_detector=trigger_detector,
    target_length=50
)

# Step 3: Configure analysis parameters
analyzer.set_frequency_bands({
    'Delta': (0.5, 4),
    'Theta': (4, 8),
    'Alpha': (8, 12),
    'Beta': (12, 30),
    'Gamma': (30, 80)
})

analyzer.set_fooof_parameters(freq_range=(1, 40))

# Step 4: Execute comprehensive analysis
channels_of_interest = ['T2', 'O1', 'F3', 'C3', 'C4']
analyzer.analyze_comprehensive(channels_to_analyze=channels_of_interest)

# Step 5: Generate comparative visualizations
analyzer.plot_fooof_comparison(
    channels=channels_of_interest,
    metric='aperiodic_exponent'
)
```

### ConnectivityAnalyzer

Converts EEG data to graph representations for network analysis and computes time-varying connectivity measures. Features professional output organization, complete analysis metadata tracking, and memory-efficient processing for any file size.

The ConnectivityAnalyzer provides **three levels of analysis complexity** to match different research needs:

1. **Simple Connectivity Analysis** - Fast correlation/coherence for exploration
2. **Detailed Connectivity Analysis** - Time-varying connectivity with visualizations  
3. **Advanced Graph Analysis** - Full graph representations for machine learning

#### Initialization
```python
processor = ConnectivityAnalyzer(
    edf_loader=loader,
    output_dir=None,            # Optional: custom output directory
    window_size=1000,           # Optional: analysis window size in samples
    adj_window_size=20000       # Optional: adjacency matrix window size (40s at 500Hz)
)
```

**Key Parameters:**
- **`window_size`**: Analysis window duration (default: 1 second at sampling rate)
- **`adj_window_size`**: Window size for adjacency calculations (default: 40 seconds for statistical robustness)
- **`output_dir`**: Custom output directory (default: subject_folder/connectivity_analysis_results/)

**Default Output Structure:**
```
data/subject_name/
├── subject_name.edf
└── connectivity_analysis_results/  # Professional organization
    ├── graphs/                     # HDF5 graph representations
    │   └── subject_graphs.h5
    ├── correlation/                # Correlation matrices
    │   ├── subject_correlation_0s-300s.pickle
    │   └── plots/                  # Correlation visualizations
    ├── coherence/
    │   ├── average/                # Average coherence matrices
    │   │   ├── subject_coherence_avg_0s-300s.pickle
    │   │   └── plots/              # Average coherence visualizations
    │   └── bands/                  # Frequency-band coherence matrices
    │       ├── subject_coherence_bands_0s-300s.pickle
    │       └── plots/              # Band-specific visualizations
    └── analysis_metadata.json     # Complete analysis tracking
```

#### Methods

##### `generate_graphs(segment_duration=180.0, start_time=None, stop_time=None, overlap_ratio=0.875)`
Creates comprehensive graph representations with adjacency matrices and node/edge features using **memory-safe HDF5 format** with segmented processing.

**Features Generated:**
- **Adjacency matrices**: Correlation, coherence, phase relationships
- **Node features**: Energy, band-specific energy across frequency bands
- **Edge features**: Connectivity measures across frequency bands
- **High temporal resolution**: 87.5% overlapping windows by default

**Key Advantages:**
- **Memory-safe**: Processes any file size without memory issues
- **Segmented processing**: Divides large files into manageable segments
- **Immediate storage**: Results saved incrementally to prevent data loss
- **Progress tracking**: Real-time progress bars and detailed logging
- **HDF5 format**: Compressed, efficient storage with selective data access

**Parameters:**
- `segment_duration` (float): Duration of each processing segment in seconds (default: 180.0)
- `start_time` (float, optional): Start time for analysis window in seconds
- `stop_time` (float, optional): End time for analysis window in seconds  
- `overlap_ratio` (float): Window overlap ratio (default: 0.875 = 87.5% overlap)

```python
# Generate comprehensive graph representations
hdf5_path = processor.generate_graphs(segment_duration=300.0)

# Analyze specific time window with high temporal resolution
hdf5_path = processor.generate_graphs(
    segment_duration=180.0,
    start_time=300,
    stop_time=900,
    overlap_ratio=0.95  # Very high resolution
)
# Output: graphs/{filename}_graphs.h5 with compressed graph data
```

**HDF5 Output Structure:**
```python
# HDF5 file contains:
{
    'adjacency_matrices': (n_windows, n_adj_types, n_electrodes, n_electrodes),
    'node_features': (n_windows,),  # Variable-length arrays
    'edge_features': (n_windows,),  # Variable-length arrays  
    'window_starts': (n_windows,),  # Timestamp for each window
    # Plus comprehensive metadata as attributes
}
```

**Loading HDF5 Results:**
```python
import h5py
import numpy as np

# Load specific data without loading entire file
with h5py.File('subject_graphs.h5', 'r') as f:
    # Load specific time range
    correlation_matrices = f['adjacency_matrices'][100:200, 1, :, :]  # Windows 100-200, correlation type
    
    # Load metadata
    sampling_freq = f.attrs['sampling_frequency']
    total_windows = f.attrs['total_windows_processed']
    
    # Load specific electrode pairs
    electrode_pair_data = f['adjacency_matrices'][:, 1, 5, 12]  # All windows, electrodes 5-12
```

##### `compute_correlation(start_time, stop_time, interval, overlap_ratio=0.0)`
Computes time-varying correlation matrices over specified time segments.

**Parameters:**
- `start_time` (float): Start time in seconds
- `stop_time` (float): End time in seconds  
- `interval` (float): Window duration for each correlation matrix in seconds
- `overlap_ratio` (float): Overlap between windows (0.0 = no overlap, 0.5 = 50% overlap)

```python
# Compute correlation every 5 seconds from 10-60s with 50% overlap
path = processor.compute_correlation(
    start_time=10.0,
    stop_time=60.0, 
    interval=5.0,
    overlap_ratio=0.5
)
```

**Output:** `correlation/{filename}_correlation_{start}s-{stop}s.pickle` containing:
```python
{
    "starts": [10.0, 12.5, 15.0, ...],  # Window start times
    "corr_matrices": [matrix1, matrix2, ...]  # Correlation matrices
}
```

##### `compute_coherence_average(start_time, stop_time, interval, overlap_ratio=0.0)`
Computes time-varying coherence matrices averaged across all frequency bands.

```python
# Simple averaged coherence analysis
path = processor.compute_coherence_average(
    start_time=10.0,
    stop_time=60.0,
    interval=5.0
)
```

**Output:** `coherence/average/{filename}_coherence_avg_{start}s-{stop}s.pickle` containing:
```python
{
    "starts": [10.0, 15.0, 20.0, ...],
    "coherence_matrices": [matrix1, matrix2, ...]  # Averaged coherence
}
```

##### `compute_coherence_bands(start_time, stop_time, interval, overlap_ratio=0.0)`
Computes detailed frequency-specific coherence analysis across EEG bands.

```python
# Detailed frequency-band coherence analysis  
path = processor.compute_coherence_bands(
    start_time=10.0,
    stop_time=60.0,
    interval=5.0,
    overlap_ratio=0.25
)
```

**Output:** `coherence/bands/{filename}_coherence_bands_{start}s-{stop}s.pickle` containing:
```python
{
    "starts": [10.0, 15.0, 20.0, ...],
    "coherence_by_band": {
        "delta": [matrix1, matrix2, ...],    # 1-4 Hz
        "theta": [matrix1, matrix2, ...],    # 4-8 Hz  
        "alpha": [matrix1, matrix2, ...],    # 8-13 Hz
        "beta": [matrix1, matrix2, ...],     # 13-30 Hz
        "gamma": [matrix1, matrix2, ...],    # 30-70 Hz
        "gammaHi": [matrix1, matrix2, ...],  # 70-100 Hz
        # Additional bands based on sampling frequency
    },
    "frequency_bands": {
        "delta": (1, 4), "theta": (4, 8), "alpha": (8, 13), ...
    }
}
```

##### `plot_connectivity_matrices(plot_types=None, time_range=None, output_subdir="plots", save_individual=True, save_summary=True, dpi=150, figsize=(10, 8))`
Generates comprehensive visualizations of connectivity matrices with **full EEG channel names** on axes.

**Parameters:**
- `plot_types` (list): Types to plot - `["correlation", "coherence_avg", "coherence_bands"]` (default: all available)
- `time_range` (tuple): `(start_time, stop_time)` to filter plots (default: all time windows)
- `output_subdir` (str): Subdirectory name for plots (default: "plots")
- `save_individual` (bool): Save individual matrix plots (default: True)
- `save_summary` (bool): Save summary/comparison plots (default: True)
- `dpi` (int): Plot resolution (default: 150)
- `figsize` (tuple): Figure size as (width, height) (default: (10, 8))

**Features:**
- **Full channel names**: All EEG channel names (Fp1, F3, C3, etc.) displayed on both axes
- **Organized output**: Plots saved alongside data in intuitive directory structure
- **Multiple plot types**: Individual matrices, time series summaries, frequency band comparisons
- **Flexible filtering**: Plot specific time ranges or connectivity types
- **High-quality output**: Publication-ready plots with proper labeling

```python
# Plot all available connectivity data
results = processor.plot_connectivity_matrices()

# Plot only correlation matrices
results = processor.plot_connectivity_matrices(plot_types=["correlation"])

# Plot coherence with time filtering
results = processor.plot_connectivity_matrices(
    plot_types=["coherence_avg", "coherence_bands"],
    time_range=(100, 200),  # Only plot 100-200 second window
    save_individual=True,
    save_summary=True
)

# Custom plot settings
results = processor.plot_connectivity_matrices(
    dpi=300,  # High resolution
    figsize=(12, 10),  # Larger plots
    output_subdir="publication_plots"
)
```

**Output Structure:**
```
graph_representation/
├── correlation/
│   ├── data.pickle
│   └── plots/
│       ├── individual/
│       │   ├── correlation_t010.0s.png
│       │   ├── correlation_t020.0s.png
│       │   └── ...
│       └── correlation_summary.png
├── coherence/
│   ├── average/
│   │   ├── data.pickle
│   │   └── plots/
│   │       ├── individual/
│   │       │   ├── coherence_avg_t010.0s.png
│   │       │   └── ...
│   │       └── coherence_avg_summary.png
│   └── bands/
│       ├── data.pickle
│       └── plots/
│           ├── individual/
│           │   ├── delta/
│           │   │   ├── delta_coherence_t010.0s.png
│           │   │   └── ...
│           │   ├── theta/
│           │   ├── alpha/
│           │   ├── beta/
│           │   └── gamma/
│           └── band_coherence_summary.png
```

#### Progressive Analysis Workflow

The ConnectivityAnalyzer supports a **progressive complexity approach** - start simple and add detail as needed:

##### Level 1: Exploratory Analysis (Fast)
```python
from yousif_raiyan_pip_package import EDFLoader, ConnectivityAnalyzer

# Load EEG data
loader = EDFLoader("data", "subject_name")
loader.load_and_plot_signals(duration=1200.0)

# Initialize processor with no overlap for speed
processor = ConnectivityAnalyzer(
    edf_loader=loader,
    window_step_ratio=1.0  # No overlap = faster processing
)

# Quick correlation overview (5-minute windows)
corr_path = processor.compute_correlation(
    start_time=0, stop_time=3600, interval_seconds=300
)

# Quick coherence overview
coh_path = processor.compute_coherence_average(
    start_time=0, stop_time=3600, interval_seconds=300
)

# Generate overview plots
processor.plot_connectivity_matrices(
    plot_types=["correlation", "coherence_avg"],
    save_individual=False,  # Only summary plots
    save_summary=True
)
```

##### Level 2: Detailed Time-Varying Analysis
```python
# Identify interesting periods from Level 1 results
# Focus on specific time ranges with higher resolution

# High-resolution analysis of interesting periods
processor_detailed = ConnectivityAnalyzer(
    edf_loader=loader,
    window_step_ratio=0.5  # 50% overlap for better resolution
)

# Detailed correlation analysis (10-second windows)
detailed_corr = processor_detailed.compute_correlation(
    start_time=100, stop_time=400, interval_seconds=10, overlap_ratio=0.5
)

# Frequency-specific coherence analysis
detailed_coh = processor_detailed.compute_coherence_bands(
    start_time=100, stop_time=400, interval_seconds=10, overlap_ratio=0.5
)

# Generate detailed visualizations
processor_detailed.plot_connectivity_matrices(
    plot_types=["correlation", "coherence_bands"],
    time_range=(100, 400),
    save_individual=True,
    save_summary=True
)
```

##### Level 3: Advanced Graph Analysis
```python
# For machine learning, GNN analysis, or comprehensive connectivity studies

# Memory-safe graph generation for any file size
processor_advanced = ConnectivityAnalyzer(
    edf_loader=loader,
    window_step_ratio=0.125  # High temporal resolution
)
hdf5_path = processor_advanced.generate_graphs_from_edf(
    segment_duration_minutes=3.0
)

# Load and analyze HDF5 results
import h5py
with h5py.File(hdf5_path, 'r') as f:
    # Access specific connectivity types
    correlations = f['adjacency_matrices'][:, 1, :, :]  # All correlation matrices
    coherences = f['adjacency_matrices'][:, 2, :, :]    # All coherence matrices
    
    # Get metadata
    n_windows = f.attrs['total_windows_processed']
    sampling_freq = f.attrs['sampling_frequency']
    
    print(f"Processed {n_windows} windows at {sampling_freq} Hz")
```

#### Method Selection Guide

**Use `compute_correlation()` when:**
- ✅ Quick data exploration and quality assessment
- ✅ Identifying periods of high/low connectivity
- ✅ Simple statistical comparisons between conditions
- ✅ Real-time or streaming analysis needs
- ✅ Memory-constrained environments

**Use `compute_coherence_average()` when:**
- ✅ Frequency-domain connectivity without band-specific details
- ✅ Robust connectivity measures (coherence is less sensitive to artifacts)
- ✅ Comparing connectivity strength across different time periods
- ✅ Preprocessing for more detailed analysis

**Use `compute_coherence_bands()` when:**
- ✅ Need frequency-specific connectivity (alpha, beta, gamma, etc.)
- ✅ Studying oscillatory coupling between brain regions
- ✅ Clinical applications requiring band-specific analysis
- ✅ Research into frequency-specific network dynamics

**Use `generate_graphs_from_edf()` when:**
- ✅ Machine learning applications (GNNs, classification)
- ✅ Complex network analysis requiring multiple connectivity measures
- ✅ Research requiring high temporal resolution connectivity tracking
- ✅ Any size EDF files (memory-safe processing)
- ✅ Production environments requiring reliability
- ✅ Need for incremental processing and progress tracking
- ✅ Long-term storage with efficient HDF5 compression

#### Complete Analysis Example
```python
from yousif_raiyan_pip_package import EDFLoader, ConnectivityAnalyzer
import h5py
import numpy as np

# Load EEG data
loader = EDFLoader("data", "Sebastian")
loader.load_and_plot_signals(duration=3600.0)  # 1 hour

# Step 1: Quick exploration (Level 1)
explorer = ConnectivityAnalyzer(edf_loader=loader, window_step_ratio=1.0)

# Overview analysis
corr_overview = explorer.compute_correlation(0, 3600, 300)  # 5-min windows
coh_overview = explorer.compute_coherence_average(0, 3600, 300)

# Generate overview plots
explorer.plot_connectivity_matrices(
    plot_types=["correlation", "coherence_avg"],
    save_summary=True
)

# Step 2: Identify interesting periods (hypothetical analysis)
# ... analyze overview results to find periods of interest ...
interesting_start, interesting_stop = 1200, 1800  # Example: 20-30 minutes

# Step 3: Detailed analysis of interesting period (Level 2)
detailed = ConnectivityAnalyzer(edf_loader=loader, window_step_ratio=0.5)

detailed_corr = detailed.compute_correlation(
    interesting_start, interesting_stop, 30, overlap_ratio=0.5
)
detailed_coh = detailed.compute_coherence_bands(
    interesting_start, interesting_stop, 30, overlap_ratio=0.5
)

# Step 4: Full graph analysis for ML (Level 3)
# Memory-safe HDF5 processing
advanced = ConnectivityAnalyzer(edf_loader=loader, window_step_ratio=0.125)
hdf5_path = advanced.generate_graphs_from_edf(segment_duration_minutes=5.0)

# Step 5: Load and analyze results
with h5py.File(hdf5_path, 'r') as f:
    # Extract features for machine learning
    correlation_features = f['adjacency_matrices'][:, 1, :, :].flatten()
    coherence_features = f['adjacency_matrices'][:, 2, :, :].flatten()
    
    # Get temporal information
    window_times = f['window_starts'][:]
    
    # Print summary
    print(f"Generated {len(window_times)} windows")
    print(f"Time range: {window_times[0]:.1f}s - {window_times[-1]:.1f}s")
    print(f"Feature dimensions: {correlation_features.shape}")

# Step 6: Generate comprehensive visualizations
advanced.plot_connectivity_matrices(
    plot_types=["correlation", "coherence_avg", "coherence_bands"],
    time_range=(interesting_start, interesting_stop),
    save_individual=True,
    save_summary=True,
    dpi=300  # High resolution for publication
)
```

## Output Structure

The package creates organized output directories with **all outputs defaulting to the subject directory**:

```
data/
└── subject_name/
    ├── subject_name.edf                    # Input EDF file
    ├── triggers.csv                        # Detected triggers
    ├── window plots/                       # Inter-trigger window plots
    │   ├── plot_0.png
    │   ├── plot_1.png
    │   └── ...
    ├── trigger.mp4                         # Video compilation
    ├── fooof_analysis/                     # Spectral parameterization outputs
    │   ├── fooof_summary.csv               # Summary across all channels
    │   ├── band_powers_summary.csv         # Band powers across