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config/data_config.yaml

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+# Data Configuration
+
+# Primary Dataset: Figshare MDD
+figshare:
+  url: "https://figshare.com/articles/dataset/EEG_Data_New/4244171"
+  data_dir: "data/raw/figshare"
+  n_subjects: 64
+  n_mdd: 34
+  n_healthy: 30
+  n_channels: 19
+  sampling_rate: 256
+  duration_sec: 300          # 5 minutes
+  conditions: ["EC", "EO"]   # Eyes Closed, Eyes Open
+
+# Secondary Dataset: Kaggle (for benchmarking)
+kaggle:
+  url: "https://www.kaggle.com/datasets/tocodeforsoul/depression-rest-eeg-features"
+  data_dir: "data/raw/kaggle"
+  n_subjects: 232
+  n_features: 31
+  note: "Pre-extracted features only"
+
+# Preprocessing
+preprocessing:
+  target_sampling_rate: 250  # Resample to this rate
+  bandpass:
+    low: 1
+    high: 45
+  notch:
+    freqs: [50, 60]         # Power line noise (50Hz EU, 60Hz US)
+  filter_order: 101
+  ica:
+    method: "picard"
+    n_components: 0.99       # Variance explained
+    max_iter: 500
+  reference: "average"
+
+# Segmentation
+segmentation:
+  epoch_length_sec: 4.0
+  overlap_ratio: 0.5         # 50% overlap
+  min_epochs_per_subject: 10
+
+# 10-20 Electrode Montage
+electrodes:
+  names: ["Fp1", "Fp2", "F7", "F3", "Fz", "F4", "F8",
+          "T3", "C3", "Cz", "C4", "T4",
+          "T5", "P3", "Pz", "P4", "T6",
+          "O1", "O2"]
+  # Standard 10-20 3D positions (normalized)
+  positions:
+    Fp1: [-0.31, 0.95, -0.03]
+    Fp2: [0.31, 0.95, -0.03]
+    F7: [-0.81, 0.59, -0.03]
+    F3: [-0.55, 0.67, 0.50]
+    Fz: [0.00, 0.71, 0.70]
+    F4: [0.55, 0.67, 0.50]
+    F8: [0.81, 0.59, -0.03]
+    T3: [-1.00, 0.00, -0.03]
+    C3: [-0.71, 0.00, 0.71]
+    Cz: [0.00, 0.00, 1.00]
+    C4: [0.71, 0.00, 0.71]
+    T4: [1.00, 0.00, -0.03]
+    T5: [-0.81, -0.59, -0.03]
+    P3: [-0.55, -0.67, 0.50]
+    Pz: [0.00, -0.71, 0.70]
+    P4: [0.55, -0.67, 0.50]
+    T6: [0.81, -0.59, -0.03]
+    O1: [-0.31, -0.95, -0.03]
+    O2: [0.31, -0.95, -0.03]
+
+# Graph Construction
+graph:
+  spatial_k_neighbors: 6     # K-nearest neighbors for spatial adjacency
+  functional_threshold: 0.5  # Correlation threshold for functional connectivity
+  combine_method: "union"    # How to combine spatial and functional edges

config/model_config.yaml

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+# Model Configuration for EEG Depression Detection
+# Optimized for <8GB VRAM
+
+# Transformer Encoder (for CWT scalograms)
+transformer:
+  d_model: 128
+  nhead: 4
+  num_layers: 4
+  dim_ff: 512
+  dropout: 0.1
+  patch_size: [8, 16]  # [freq, time]
+  max_seq_len: 65      # 64 patches + 1 CLS token
+
+# Graph Attention Network (for electrode relationships)
+gnn:
+  num_nodes: 19           # 10-20 montage electrodes
+  node_feat_dim: 576      # WPD features per electrode
+  hidden_dim: 128
+  num_heads: 4
+  num_layers: 3
+  dropout: 0.3
+  pooling_ratio: 0.5
+  adjacency_type: "hybrid"  # spatial + functional
+
+# Attention-Based Fusion
+fusion:
+  trans_dim: 128
+  gnn_dim: 128
+  fusion_dim: 128
+  num_heads: 4
+  dropout: 0.1
+  use_gating: true
+  strategy: "cross_attention"
+
+# Classification Head
+classifier:
+  input_dim: 128
+  hidden_dims: [64, 32]
+  dropout_rates: [0.5, 0.3]
+  num_classes: 1  # Binary classification
+
+# Wavelet Feature Extraction
+wpd:
+  wavelets: ["db4", "sym5", "coif3"]
+  level: 5
+  features: ["energy", "entropy", "log_energy", "mean", "std", "skewness", "kurtosis"]
+
+cwt:
+  wavelet: "cmor1.5-1.0"
+  freq_range: [1, 45]
+  num_scales: 64
+  output_size: [64, 128]  # [freq, time]

config/training_config.yaml

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+# Training Configuration
+# Optimized for <8GB VRAM
+
+# Optimizer
+optimizer:
+  name: "AdamW"
+  lr: 0.0001
+  weight_decay: 0.01
+  betas: [0.9, 0.999]
+
+# Learning Rate Scheduler
+scheduler:
+  name: "CosineAnnealingWarmRestarts"
+  T_0: 10
+  T_mult: 2
+  eta_min: 0.000001
+
+# Training
+training:
+  epochs: 100
+  batch_size: 16
+  gradient_accumulation_steps: 2  # Effective batch size = 32
+  gradient_clip: 1.0
+  early_stopping_patience: 15
+  early_stopping_delta: 0.001
+
+# Memory Optimization
+memory:
+  mixed_precision: true           # FP16 training
+  gradient_checkpointing: true    # Trade compute for memory
+  pin_memory: true
+  num_workers: 4
+  prefetch_factor: 2
+
+# Loss Function
+loss:
+  name: "BCEWithLogitsLoss"
+  label_smoothing: 0.1
+  class_weight: "balanced"       # Compute from training data
+
+# Data Augmentation
+augmentation:
+  enabled: true
+  time_shift_ratio: 0.1          # Max 10% shift
+  time_warp_sigma: 0.2
+  channel_dropout_prob: 0.1
+  noise_snr_db: 20
+
+# Cross-Validation
+cross_validation:
+  strategy: "loso"               # Leave-One-Subject-Out
+  n_splits: 5                    # For stratified k-fold fallback
+  shuffle: true
+  random_state: 42
+
+# Checkpointing
+checkpointing:
+  save_best: true
+  save_last: true
+  save_every_n_epochs: 10
+  checkpoint_dir: "checkpoints"
+
+# Logging
+logging:
+  log_every_n_steps: 10
+  log_dir: "logs"
+  use_wandb: false               # Set to true if using Weights & Biases

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