DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses

Mojtaba Khomami Abadi, Ramanathan Subramanian, Seyed Mostafa Kia, Paolo Avesani, Ioannis Patras, Nicu Sebe

Research output: Contribution to journalArticlepeer-review

230 Citations (Scopus)


In this work, we present DECAF - a multimodal data set for decoding user physiological responses to affective multimedia content. Different from data sets such as DEAP [15] and MAHNOB-HCI [31], DECAF contains (1) brain signals acquired using the Magnetoencephalogram (MEG) sensor, which requires little physical contact with the user's scalp and consequently facilitates naturalistic affective response, and (2) explicit and implicit emotional responses of 30 participants to 40 one-minute music video segments used in [15] and 36 movie clips, thereby enabling comparisons between the EEG versus MEG modalities as well as movie versus music stimuli for affect recognition. In addition to MEG data, DECAF comprises synchronously recorded near-infra-red (NIR) facial videos, horizontal Electrooculogram (hEOG), Electrocardiogram (ECG), and trapezius-Electromyogram (tEMG) peripheral physiological responses. To demonstrate DECAF's utility, we present (i) a detailed analysis of the correlations between participants' self-assessments and their physiological responses and (ii) single-trial classification results for valence, arousal and dominance, with performance evaluation against existing data sets. DECAF also contains time-continuous emotion annotations for movie clips from seven users, which we use to demonstrate dynamic emotion prediction.

Original languageEnglish
Article number7010926
Pages (from-to)209-222
Number of pages14
JournalIEEE Transactions on Affective Computing
Issue number3
Publication statusPublished - 1 Jul 2015
Externally publishedYes


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