Part of MONSTER: https://arxiv.org/abs/2502.15122.

DreamerA
Category	EEG
Num. Examples	170,246
Num. Channels	14
Length	256
Sampling Freq.	128 Hz
Num. Classes	2
License	Other
Citations	[1] [2] [3]

Dreamer is a multimodal dataset that includes electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect elicitation using audio-visual stimuli [1], captured with a 14-channel Emotiv EPOC headset at a sampling rate of 128 Hz. It consists of data recording from 23 participants, along with their self-assessments of affective states (valence, arousal, and dominance) after each stimulus. For our classification task, we focus on the arousal and valence labels, referred to as DreamerA and DreamerV respectively. The processed datasets both consist of 170,246 multivariate time series each of length 256 (i.e., representing 2 seconds of data per time series at a sampling rate of 128 Hz).

The dataset is publicly available [2], and we utilize the Torcheeg toolkit for preprocessing, including signal cropping and low-pass and high-pass filtering [3]. Note that only EEG data is analyzed in this study, with ECG signals excluded. Labels for arousal and valence are binarized, assigning values below 3 to class 1 and values of 3 or higher to class 2, and has been split into cross-validation folds based on participant.

[1] Stamos Katsigiannis and Naeem Ramzan. (2017) Dreamer: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE Journal of Biomedical and Health Informatics, 22(1):98–107.

[2] Stamos Katsigiannis and Naeem Ramzan. (2017). Dreamer: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. https://zenodo.org/records/546113.

[3] Zhi Zhang, Sheng-Hua Zhong, and Yan Liu. (2024). TorchEEGEMO: A deep learning toolbox towards EEG-based emotion recognition. Expert Systems with Applications.