/MFA-LR

This repository contains the code for our proposed method "Multi-source Feature Alignment and Label Rectification" (MFA-LR), which has been published in the paper "Learning a Robust Unified Domain Adaptation Framework for Cross-subject EEG-based Emotion Recognition."

Primary LanguagePython

MFA-LR

This repository contains the code for our proposed method "Multi-source Feature Alignment and Label Rectification" (MFA-LR), which has been published in the paper "Learning a Robust Unified Domain Adaptation Framework for Cross-subject EEG-based Emotion Recognition."

Cite

If the published code is useful for you, please cite our paper.

@article{JIMENEZGUARNEROS2023,
title = {Learning a robust unified domain adaptation framework for cross-subject EEG-based emotion recognition},
journal = {Biomedical Signal Processing and Control},
volume = {86},
pages = {105138},
year = {2023},
issn = {1746-8094},
doi = {https://doi.org/10.1016/j.bspc.2023.105138},
url = {https://www.sciencedirect.com/science/article/pii/S1746809423005712},
author = {Magdiel Jiménez-Guarneros and Gibran Fuentes-Pineda}
}

Dependencies

We used PyTorch 1.9.1 with Python 3.6 for our implementation. All experiments were performed with a PC Intel(R) Core (TM) i7 with an NVIDIA GeForce GTX 1080 GPU and Ubuntu Linux v18.04 (LTS).