MFA-LR

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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."

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# 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).

MFA-LR

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