!! This repository is currently under construction. More code will be added over the next few weeks !!
SparseMambaNet: A Novel Architecture Integrating Bi-Mamba and Mixture of Experts for Efficient EEG-Based Lie Detection
Hanbeot Park†1, Yunjeong Cho†1, Hunhee Kim*
- *Correspondence
- †These authors contributed equally to this work.
This repository contains the code for the offline implementation of the EEG-based lie detection methods described in the paper "SparseMambaNet: A Novel Architecture Integrating Bi-Mamba and Mixture of Experts for Efficient EEG-Based Lie Detection". A demo to classify deception from scalp EEG signals during the CQT (Comparison Question Technique) task is provided.
- Python environment: Set up a new venv environment using the provided
requirements.txtas follows:
pip install -r requirments.txt
This will install all the required Python packages to run this code (installation will take around 10-15 minutes).
- System requirements: This code runs on a GPU for optimal performance. It has been tested on Linux (22.04 LTS) with RTX 4090 GPU.
The EEG dataset is not publicly available due to privacy concerns.
It's soon as possible
The README files within each folder provide further information.