REFED: A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset

REFED: A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset

Xiaojun Ning¹, Jing Wang^1,∗, Zhiyang Feng¹, Tianzuo Xin¹, Shuo Zhang¹, Shaoqi Zhang¹, Zheng Lian², Yi Ding³, Youfang Lin¹, Ziyu Jia^2,∗

¹ Beijing Jiaotong University
² Institute of Automation, Chinese Academy of Sciences
³ Nanyang Technological University
NeurIPS 2025
^*Corresponding Authors

Paper

Dataset Code

The EEG-fNIRS recording system and experiment paradigm overview.

Abstract

Affective brain-computer interfaces (aBCIs) play a crucial role in personalized human–computer interaction and neurofeedback modulation. To develop practical and effective aBCI paradigms and to investigate the spatial-temporal dynamics of brain activity under emotional inducement, portable electroencephalography (EEG) signals have been widely adopted. To further enhance spatial-temporal perception, functional near-infrared spectroscopy (fNIRS) has attracted increasing interest in the aBCI field and has been explored in combination with EEG. However, existing datasets typically provide only static fixation labels, overlooking the dynamic changes in subjects' emotions. Notably, some studies have attempted to collect continuously annotated emotional data, but they have recorded only peripheral physiological signals without directly observing brain activity, limiting insight into underlying neural states under different emotions. To address these challenges, we present the Real-time labeled EEG-fNIRS Emotion Dataset (REFED). REFED simultaneously records brain signals from both EEG and fNIRS modalities while providing continuous, real-time annotations of valence and arousal. The results of the data analysis demonstrate the effectiveness of emotion inducement and the reliability of real-time annotation. This dataset offers the first possibility for studying the neural-vascular coupling mechanism under emotional evolution and for developing dynamic, robust affective BCIs.

License

The REFED dataset and code are made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC-BY-NC-SA 4.0) International License. Detailed license terms can be found at https://creativecommons.org/licenses/by-nc-sa/4.0/ .

BibTeX

@inproceedings{NEURIPS2025_REFED,
  title = {REFED: A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset},
  author = {Ning, Xiaojun and Wang, Jing and Feng, Zhiyang and Xin, Tianzuo and Zhang, Shuo and Zhang, Shaoqi and Lian, Zheng and Ding, Yi and Lin, Youfang and Jia, Ziyu},
  booktitle = {The Thirty-Ninth Annual Conference on Neural Information Processing Systms},
  year = {2025}
}