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The Dataset on Emotion with Naturalistic Stimuli (DENS) contains EEG, ECG and EMG recordings from 40 participants who were emotionally stimulated with naturalistic multimedia videos. The videos provide context for the situated conceptualization of emotions. Participants gave self-assessment ratings of valence, arousal, dominance, liking, familiarity, relevance and emotion category. The dataset also includes stimuli, code for data collection, validation and pre-processing.

Participants

40

Channels

128

Citations

3

Size

4.46 GB

Version

v1.0.0

Updated

Jun 22, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003483-blue…

Participants

21

Size

68.3 GB

Version

v1.0.0

Updated

Jun 21, 2026

EEG recordings from 8 healthy adults using a real-time, online affective music brain-computer interface. The system identified each listener's emotional state and modified synthetically generated 60 s music clips on the fly to induce target affective states defined by valence and arousal. The dataset includes self-reported valence and arousal and is one of three connected datasets from the BCMI-MIdAS project.

Participants

8

Channels

32 (10-10)

Size

2.60 GB

Version

v1.0.0

Updated

Jun 21, 2026

…information on participants' sleepiness and mood states. （Please note here Session 1…

Participants

71

Channels

61 (10-10)

Citations

43

Size

8.29 GB

Version

v1.0.0

Updated

Jun 25, 2026

…Second, they provide a comprehensive picture of cognitive functioning, including mood regulation…

Participants

251

Citations

26

Size

666 GB

Version

v1.0.0

Updated

Jun 14, 2026

EEG recordings from 31 healthy adults who listened to 40 twelve-second film-score music clips chosen to induce a range of emotional states. The dataset includes unprocessed EEG at 1 kHz, resting-state runs, and participants' self-reported induced emotional responses. It supports investigation of the neural correlates of music-induced emotion and was collected within the BCMI-MIdAS project.

Participants

31

Channels

19 (10-20)

Size

3.35 GB

Version

v1.0.0

Updated

Jun 21, 2026

…originals Reference --------- Ghassemi, M.M., Moody, B.E., Lehman, L.H., Song…

Participants

1983

Channels

6 (other)

Citations

91

Size

401 GB

Version

v1.1.0

Updated

Jun 3, 2026

EEG dataset investigating whether images of stylized (cartoon-like) faces with exaggerated visual features improve event-related potential (ERP) correlates of emotion relative to ordinary human faces. The stylized faces elicit an enhanced N170 component associated with facial recognition encoding. The findings suggest that consistent stimulus transformations could improve affect detection from EEG in affective brain-computer interface design.

Participants

26

Channels

28 (10-10)

Citations

1

Size

2.46 GB

Version

v1.0.0

Updated

Jun 24, 2026

High-density EEG recorded from resting, eyes-closed participants who used voice-guided imagery to enter 15 distinct emotional states, including positive states such as joy and negative states such as sadness and anger. Participants pressed a button when they began to feel the target emotion and again when the feeling waned, with relaxation instructions between the imagery periods. The dataset supports research on EEG-based emotion recognition and brain state dynamics during emotion imagination.

Participants

34

Channels

224 (biosemi)

Citations

73

Size

36.0 GB

Version

v1.0.0

Updated

Jun 21, 2026

…set of the George B. Moody PhysioNet Challenge 2023, for which these…

Participants

342

Size

1.54 TB

Version

v1.0.0

Updated

Oct 5, 2026