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README.md

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-license: mit
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+---
+metadata:
+  license: mit
+---
+
+# Multi-Window EEG Models
+
+This repository hosts pre-trained PyTorch models (.pth files) for temporal analysis of EEG signals in object category decoding. The models are trained on the [Alljoined/05_125](https://huggingface.co/datasets/Alljoined/05_125) dataset, using COCO 2017 images as stimuli. Each model processes a specific time window post-stimulus onset to capture different stages of visual processing:
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+- **EarlyVisual (50-150ms)**: Early visual features (e.g., edges, basic shapes). AUROC: ~0.59
+- **MidFeature (150-250ms)**: Mid-level object parts (e.g., N170-like responses). AUROC: ~0.97
+- **LateSemantic (250-350ms)**: Late semantic integration (e.g., N400/P300). AUROC: ~0.67
+- **EarlyCombined (50-250ms)**: Combined early + mid processing. AUROC: ~0.97
+- **FullWindow (50-350ms)**: Full baseline window. AUROC: ~0.97
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+These models use a hybrid CNN-Transformer architecture for multi-label classification over 38 COCO categories (animals, vehicles, food, outdoor objects). They detect weak category-specific signals in noisy EEG data.
+
+## Usage
+
+Load a model with PyTorch:
+
+```python
+import torch
+
+# Example: Load MidFeature model
+checkpoint = torch.load("model_150_250ms_MidFeature.pth", map_location="cpu")
+model = HybridCNNTransformer(n_timepoints=52)  # Exact points from window
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+# Inference on EEG window (shape: [1, 64, n_timepoints])
+logits = model(eeg_tensor)
+probs = torch.sigmoid(logits)
+top_categories = torch.topk(probs, k=20).indices