REFED (A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset) holds simultaneously recorded EEG and fNIRS from 32 healthy participants watching 15 emotion-eliciting video clips, with valence and arousal annotated continuously, in real time, by the participants themselves. Released on Hugging Face by Xiaojun Ning, Ziyu Jia and colleagues under CC BY-NC-SA 4.0, behind a gate that asks users to agree to non-commercial use and to cause no harm to human subjects, and described at NeurIPS 2025 (Datasets and Benchmarks Track). Only stored EEG clips from two nominal participant folders are used here.
Also known asREFED · A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset · REFED2025/REFED-dataset · Ning et al. (2025)
Shared videos, a folder held out (familiar stimuli)
Equal-axis mean absolute error, raw self-report scale (lower is better)
107.182
2 nominal participant folders
Source global meanreference or comparator, not a decoding model
Shared videos, a folder held out (familiar stimuli)
Equal-axis mean absolute error, raw self-report scale (lower is better)
40.287
2 nominal participant folders
Source video lookupreference or comparator, not a decoding model
Shared videos, a folder held out (familiar stimuli)
Equal-axis mean absolute error, raw self-report scale (lower is better)
27.325
2 nominal participant folders
Duration / numeric-video-ID ridgereference or comparator, not a decoding model
Shared videos, a folder held out (familiar stimuli)
Equal-axis mean absolute error, raw self-report scale (lower is better)
37.910
2 nominal participant folders
EEG Ridge (relative band power)
Folder and video IDs held out together
Equal-axis mean absolute error, raw self-report scale (lower is better)
91.984
2 nominal participant folders
Source global meanreference or comparator, not a decoding model
Folder and video IDs held out together
Equal-axis mean absolute error, raw self-report scale (lower is better)
41.032
2 nominal participant folders
Source video lookupreference or comparator, not a decoding model
Folder and video IDs held out together
Equal-axis mean absolute error, raw self-report scale (lower is better)
41.032
2 nominal participant folders
Duration / numeric-video-ID ridgereference or comparator, not a decoding model
Folder and video IDs held out together
Equal-axis mean absolute error, raw self-report scale (lower is better)
47.098
2 nominal participant folders
2 nominal participant folders with the same 15 videos: 30 clips, not as many independent people, so no interval or significance test is reported. EEG Ridge has a larger error than every source-only control in both conditions. With shared videos, the same-video lookup has the smallest error, because familiar stimuli carry the prediction; held out together, it has no matching video and equals the global mean. Targets are the mean of each clip’s stored valence and arousal labels on the published raw scale, not SAM scores. Stored EEG units, the EEG-to-rating clock and the crop end points are unresolved, and the rating means were seen before this design. No physiological, neural or population claim; outside every leaderboard.
Source and licence
REFED emotion dataset (EEG-fNIRS)
CreditXiaojun Ning, Jing Wang, Zhiyang Feng, Tianzuo Xin, Shuo Zhang, Shaoqi Zhang, Zheng Lian, Yi Ding, Youfang Lin and Ziyu Jia · REFED: A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset, NeurIPS 2025 Datasets and Benchmarks Track. Data: REFED2025/REFED-dataset on Hugging Face, revision 0298355c2823946ac5800b2ec12a082bf2dcd01b, CC BY-NC-SA 4.0, gated. Derived analysis by BCI Report; not endorsed by the authors.
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data remain their creators’.
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