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Large Brain Models with Test Time Training

NeuroTTT 최신버젼 다운로드

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# NeuroTTT

_Bridging Pretrain–Downstream Task Misalignment in EEG Foundation Models via Test-Time Training_

[![Paper](https://img.shields.io/badge/arXiv-Coming%20Soon-red)](https://arxiv.org/)
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</div>

## 🎯 Overview

**NeuroTTT** is a novel framework that bridges the gap between pretrained EEG foundation models and downstream tasks through **Test-Time Training (TTT)**. Our approach addresses the fundamental challenge of domain misalignment in EEG foundation models by introducing:

- **Domain-specific self-supervised fine-tuning** that augments foundation models with task-relevant objectives
- **Test-time training** for individual unlabeled test samples during inference
- **Prediction entropy minimization (Tent)** for continual model calibration

The framework integrates multiple state-of-the-art components:
- **CBraMod**: A Criss-Cross Brain Foundation Model for EEG decoding
- **Tent**: Fully test-time adaptation by entropy minimization
- **Multiple pretext tasks**: Band filtering, temporal ordering, channel masking, and more

<p align="center">
    🔨&nbsp;<a href="#-installation">Installation</a>
    | 🚀&nbsp;<a href="#-quick-start">Quick Start</a>
    | 📊&nbsp;<a href="#-supported-datasets">Datasets</a>
    | 🏋️&nbsp;<a href="#-training">Training</a>
    | 🧪&nbsp;<a href="#-test-time-adaptation">Test-Time Adaptation</a>
    | 📖&nbsp;<a href="#-documentation">Documentation</a>
    | 🔗&nbsp;<a href="#-citation">Citation</a>
</p>

## 🔗 Citation

If you use NeuroTTT in your research, please cite:

```bibtex
@misc{wang2025neurotttbridgingpretrainingdownstreamtask,
      title={NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training}, 
      author={Suli Wang and Yangshen Deng and Zhenghua Bao and Xinyu Zhan and Yiqun Duan},
      year={2025},
      eprint={2509.26301},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2509.26301}, 
}
```

## ✨ Key Features

- **🧠 Foundation Model Integration**: Built on CBraMod, a state-of-the-art EEG foundation model
- **🔄 Test-Time Training**: Self-supervised adaptation during inference without labeled data
- **🎯 Multiple Pretext Tasks**: Band filtering, temporal ordering, channel masking, phase prediction
- **📊 Comprehensive Dataset Support**: Multiple EEG datasets across three domains
- **⚡ Efficient Implementation**: Optimized for both research and practical applications
- **🔧 Flexible Configuration**: UV-based configuration system for easy experimentation

## 🔨 Installation

### Prerequisites

- Python 3.10+
- CUDA-compatible GPU (recommended)
- [uv](https://github.com/astral-sh/uv) package manager

### Quick Installation

1. **Install uv package manager**:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

2. **Clone the repository**:
```bash
git clone https://github.com/wsl2000/NeuroTTT.git
cd NeuroTTT
```

3. **Initialize environment**:
```bash
bash init_env.sh
```

4. **Activate environment**:
```bash
source .venv/bin/activate
```

## 🚀 Quick Start

### Fine-tuning Example

```python
DATASET_PROCESSED_SPEECH="${DATASET_PROCESSED_ROOT}BCIC2020-3/"
PRETRAINED_WEIGHTS="./CBraMod/pretrained_weights/pretrained_weights.pth"
MODEL_WEIGHTS_ROOT="./model_weights/BCIC2020-3/"
SPLIT_CONFIG="./configs/speech_dataset_default.yaml"

# Fine-tune on BCIC2020-3 dataset
python CBraMod/finetune_main.py \
    --epochs 50 \
    --cuda 0 \
    --seed 8888 \
    --batch_size 64 \
    --lr 5e-4 \
    --split_config ${SPLIT_CONFIG} \
    --multi_lr 0 \
    --weight_decay 5e-2 \
    --dropout 0.1 \
    \
    --downstream_dataset BCIC2020-3 \
    --datasets_dir ${DATASET_PROCESSED_SPEECH} \
    --num_of_classes 5 \
    --model_dir ${MODEL_WEIGHTS_ROOT} \
    --use_pretrained_weights True \
    --foundation_dir ${PRETRAINED_WEIGHTS} \
    --classifier all_patch_reps \
    --pretext none
```

## 📊 Supported Datasets

NeuroTTT provides built-in support for three EEG datasets. You can extend it to additional datasets, and we’ve retained CBraMod’s original dataset support for convenience:

### NeuroTTT
- **BCIC2020-3**: Imagined Speech Classification (5 classes)
- **BCIC-IV-2a**: Motor imagery classification (4 classes)
- **MentalArithmetic**: Mental workload (binary classification)

### Clinical Applications
- **CHB-MIT**: Seizure detection (binary classification)
- **TUAB**: Abnormal EEG detection (binary classification)
- **TUEV**: EEG evaluation (multi-class)
- **ISRUC**: Sleep stage classification (5 stages)

### Cognitive & Emotional States
- **SEED-V**: Emotion recognition (5 emotions)
- **SEED-VIG**: Vigilance estimation (regression)
- **Faced**: Face processing (multi-class)
- **Mumtaz2016**: Mental state classification (binary)

### Stress & Workload
- **Stress Dataset**: Stress level classification

## 🏋️ Training

### 1. Fine-tuning with Pretext Tasks

Fine-tune with self-supervised pretext tasks:

```bash
# With band filtering pretext task
python CBraMod/finetune_main.py \
    --downstream_dataset MentalArithmetic \
    --pretext band \
    --pretext_weight_band 0.1 \
    --epochs 20 \
    --lr 1e-4

# With multiple pretext tasks
python CBraMod/finetune_main.py \
    --downstream_dataset BCIC-IV-2a \
    --pretext all \
    --pretext_weight_band 0.2 \
    --pretext_weight_temporal 0.6 \
    --epochs 20
```

### 3. Available Pretext Tasks

- **`band`**: Frequency band filtering and reconstruction
- **`temporal`**: Temporal order prediction
- **`channel`**: Channel masking and reconstruction
- **`phase`**: Phase prediction tasks
- **`reverse`**: Reverse sequence prediction
- **`all`**: Combination of multiple pretext tasks

## 🧪 Test-Time Adaptation

### Self-Supervised Test-Time Training

Adapt the model to individual test samples using self-supervised objectives:

```bash
python CBraMod/ttt_main.py \
    --downstream_dataset BCIC2020-3 \
    --model_path ./model_weights/BCIC2020-3/best_model.pth \
    --ttt_lr 1e-4 \
    --ttt_steps 5 \
    --pretext band \
    --split_config ./configs/speech_dataset_default.yaml
```

### Entropy Minimization (Tent)

Apply Tent for continual adaptation during inference:

```bash
python CBraMod/tta_main.py \
    --test_time_method tent \
    --downstream_dataset MentalArithmetic \
    --model_path ./model_weights/MentalArithmetic/best_model.pth \
    --lr 1e-3 \
    --steps 1 \
    --episodic
```

### Test-Time Methods

- **`source`**: No adaptation (baseline)
- **`tent`**: Entropy minimization adaptation
- **`norm`**: Batch normalization statistics update

## 📁 Project Structure

```
NeuroTTT/
├── CBraMod/                    # CBraMod foundation model
│   ├── models/                 # Model architectures
│   ├── datasets/               # Dataset loaders
│   ├── preprocessing/          # Data preprocessing scripts
│   ├── pretrained_weights/     # Pretrained model weights
│   ├── finetune_main.py       # Fine-tuning script
│   ├── ttt_main.py            # Test-time training script
│   └── tta_main.py            # Test-time adaptation script
├── tent/                       # Tent implementation
│   ├── tent.py                # Core Tent algorithm
│   ├── norm.py                # Normalization methods
│   └── cifar10c.py            # Example usage
├── configs/                    # Configuration files
│   ├── speech_dataset_default.yaml
│   └── ...
├── figure/                     # Figures and diagrams
├── init_env.sh                # Environment setup script
└── README.md                  # This file
```

## ⚙️ Configuration

### Dataset Configuration

Configure dataset splits using YAML files in the `configs/` directory:

```yaml
# Example: speech_dataset_default.yaml
trial_range: [0, 399]
subject_range: [1, 16]
test_split_by: trial
val_split_by: trial
test_split: [350, 399]
val_split: [300, 349]
```

### Model Configuration

Key parameters for model configuration:

- **`classifier`**: `all_patch_reps`, `all_patch_reps_onelayer`, `avgpooling_patch_reps`
- **`dropout`**: Dropout rate (default: 0.1)
- **`pretext`**: Pretext task selection
- **`pretext_weight_*`**: Weights for different pretext tasks

## 🙏 Acknowledgments

- **CBraMod**: Original implementation by [wjq-learning](https://github.com/wjq-learning/CBraMod)
- **Tent**: Original implementation from [ICLR 2021 paper](https://openreview.net/forum?id=uXl3bZLkr3c)
- All dataset providers and the EEG research community

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