DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices
By Stamos Katsigiannis, Naeem Ramzan. IEEE journal of biomedical and health informatics, 2018
974 citations recorded by the source corpus.
In this paper, we present DREAMER, a multimodal database consisting of electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect elicitation by means of audio-visual stimuli. Signals from 23 participants were recorded along with the participants self-assessment of their affective state after each stimuli, in terms of valence, arousal, and dominance. All the signals were captured using portable, wearable, wireless, low-cost, and off-the-shelf equipment that has the potential to allow the use of affective computing methods in everyday applications. A baseline for participant-wise affect recognition using EEG and ECG-based features, as well as their fusion, was established through supervised classification experiments using support vector machines (SVMs). The self-assessment of the participants was evaluated through comparison with the self-assessments from another study using the same audio-visual stimuli. Classification results for valence, arousal, and dominance of the proposed database are comparable to the ones achieved for other databases that use nonportable, expensive, medical grade devices. These results indicate the prospects of using low-cost devices for affect recognition applications. The proposed database will be made publicly available in order to allow researchers to achieve a more thorough evaluation of the suitability of these capturing devices for affect recognition applications.
Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/1d6faea849d6d05bb74f84622302976a2305a17f
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Citations in the corpus
- EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, methodology): “The first one is the SJTU Emotion EEG Database (SEED) [18] while the second one is the DREAMER [53].”
- EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, methodology): “EMOTION analysis is important in daily life, particularly in the human-computer interaction field [1], [2], [3], [4], [5].”
- EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “2020 Cui et al. [144] DEAP, DREAMER an end-to-end regional-asymmetric CNN (RACNN) consists of temporal (1D-CNN), regional (2D-CNN) and asymmetric feature extractors (ADL) 2 classes, subject-dependent modeling (DEAP-Valence: 96.65%, DEAPArousal: 97.11%, DREAMER-Valence: 95.55%, DREAMER-Arousal: 97.01”
- Self-Supervised ECG Representation Learning for Emotion Recognition (2020, methodology): “We use four publicly available datasets, AMIGOS [5], DREAMER [23], WESAD [24], and SWELL [10] to perform emotion recognition with ECG.”
- Multimodal Large Language Models: A Survey (2023, neutral): “We also presented a compilation of commonly used datasets that can provide valuable experimentation and evaluation resources.”
- EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, background): “As for video-related contents, SEED [124–126], DREAMER [127], and ASCERTAIN [107] do provide their video database either openly or upon request.”
- Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition (2021, methodology): “Arousal Valence Dominance SVM [46] 62.3/– 62.5/– 61.8/– SVM [53] 68.8/24.9 60.1/33.3 75.8/20.8 GraphSLDA [53] 68.1/17.5 57.7/13.9 73.9/15.9 GSCCA [53] 70.”