Introduction

Welcome to NEST - the Neural EEG Sequence Transducer framework for decoding brain signals into natural language.

What is NEST?

NEST (Neural EEG Sequence Transducer) is an open-source deep learning framework designed to decode EEG brain signals encoding eye-related brain activity (EEG) into natural language text. Built on PyTorch, NEST provides researchers and developers with powerful tools to train, evaluate, and deploy EEG-to-text models.

Key Features

• State-of-the-art transformer architecture optimized for EEG signals
• Pre-trained models on ZuCo dataset with 73.9% accuracy
• Real-time inference capability with GPU acceleration
• Extensive documentation and example notebooks

Quick Start

Install NEST using pip:

pip install nest-eeg

Basic usage example:

# python
from nest import NESTModel, load_pretrained

# Load pre-trained model
model = NESTModel.from_pretrained("nest-base")

# Decode EEG signals
eeg = NEST.decode(eeg_signals)
predictions = model.predict("The brain thinks in ...")

Installation

NEST can be installed via pip or from source:

# Install via pip
pip install nest-eeg

# Or install from source
git clone https://github.com/wazder/NEST.git
cd NEST
pip install -e .

Requirements

• Python 3.8+
• PyTorch 2.0+
• CUDA 11.8+ (for GPU acceleration)
• 8GB+ GPU memory recommended

Configuration

NEST uses YAML configuration files for model and training settings:

# configs/model.yaml
model:
  type: nest
  encoder:
    hidden_size: 512
    num_layers: 6
    num_heads: 8
  decoder:
    hidden_size: 512
    num_layers: 6
    vocab_size: 50000

training:
  batch_size: 32
  learning_rate: 1e-4
  epochs: 100

Models API

The core model classes:

from nest.models import NESTModel, NESTEncoder, NESTDecoder

# Create model from config
model = NESTModel.from_config("configs/model.yaml")

# Or load pre-trained
model = NESTModel.from_pretrained("nest-base")

Training

Train your own model:

from nest import Trainer, DataModule

# Setup data
data = DataModule("path/to/zuco")

# Create trainer
trainer = Trainer(
    model=model,
    data=data,
    config="configs/training.yaml"
)

# Start training
trainer.fit()