DEAP: A Database for Emotion Analysis ;Using Physiological Signals

By Sander Koelstra, Christian Mühl, Mohammad Soleymani, Jong-Seok Lee, Ashkan Yazdani, Touradj Ebrahimi, Thierry Pun, Anton Nijholt, Ioannis Patras. IEEE Transactions on Affective Computing, 2012

4,571 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/5a2892f91addeea2f4600d28b23e684be32f5b2c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Experimental and Cognitive Psychology

In Social Psychology

Citations in the corpus

A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “, [Bailenson et al. 2008; Glodek et al. 2013; Koelstra et al. 2012; Soleymani et al. 2012; Wöllmer et al. 2013a]) or (c) naturalistic displays of affect (i.”
Emotion Recognition based on EEG using LSTM Recurrent Neural Network (2017, methodology): “DEAP [2] dataset is used to verify the algorithm proposed in this paper.”
Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection (2016, background): “Koelstra et al. [28] employed central and peripheral physiological signals for emotional tagging of music videos.”
Physiological Signals-based Emotion Recognition via High-order Correlation Learning (2019, methodology): “Therefore, methods based on this mode is more reliable and universally applicable [20].”
DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices (2018, methodology): “2) ECG-based features: Many studies have shown that features extracted from ECG signals correlate with changes in the affective state of a person [1], [2], [12], with the most con-”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “A popular EEG-based emotion classification database DEAP [118] was applied to the framework, and the PSD features performance was enhanced by the connectivity features, with PLV matrices obtaining 99.”
EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, background): “…and IAPS at 6.25%, and lastly, IADS, DREAMER, MediaEval, Quran Verse, DECAF, and NAPS all at 3.13%. most prominent use for music stimuli all comes from the DEAP dataset [121] which is highly regarded and commonly referred to for its open access for researchers to conduct their research studies.”
Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, methodology): “The datasets that are used in more than one study within this review include an emotion recognition dataset ([56], various subsets used in ten studies), a mental workload dataset ([57], used in four studies), two motor imagery datasets from the same BCI competition ([58], one dataset used in six stu”
EEG-based classification of positive and negative affective states (2014, methodology): “[38] A wide range of machine learning methods have been employed in the field of emotion recognition ranging from linear discriminant function [28] over k-nearest”
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge (2016, background): “Physiological signals are known to be well correlated with emotion [26, 25], despite not being directly perceptible the way audio-visual are.”
AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups (2017, methodology): “A FFECTIVE computing aims for the detection, modeling and synthesis of human emotional cues in Human-Computer Interaction [1].”
A Review of Emotion Recognition Using Physiological Signals (2018, background): “In [16], 32 subjects watched 40 one-minute long music videos.”
EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “After watching each ve feelings of valence, on a continuous scale from 1 to 9. by DEAP were used in the .”
Emotions Recognition Using EEG Signals: A Survey (2019, methodology): “The majority used the dataset for emotion analysis using EEG, physiological and video signals (DEAP) [137], and the remaining the Mahnob HCI dataset [70].”
Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals (2018, background): “, low and high) or more classes by selecting the threshold of a 9-point self-assessment rating scale [2].”
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “Many studies of DEAP emotion recognition [54] focus on binary (high vs. low arousal or positive vs. negative) classiﬁcation problems [55–59], and the target emotional labels are typically determined by utilizing a simple hard threshold of the subjects’ subjective rating data.”
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, neutral): “The DEAP dataset includes the EEG and peripheral physiological signals of 32 participants when watching 40 one-minute music videos.”
Wavelet-based emotion recognition system using EEG signal (2017, methodology): “5, the level of arousal/valence is considered low [17].”
EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, methodology): “[4] presented DEAP database for the analysis of human affective states, and extracted the spectral power features of five frequency bands from 32 participants.”