NEMO: A Database for Emotion Analysis Using Functional Near-Infrared Spectroscopy

• Published in: IEEE transactions on affective computing Vol. 15; no. 3; pp. 1166 - 1177
• Main Authors: Spape, Michiel, Makela, Kalle, Ruotsalo, Tuukka
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affect (Psychology); Affective computing; Annotations; Arousal; Biomedical monitoring; Classification; Datasets; Decoding; Electroencephalography; emotion classification; FNIRS; Functional near-infrared spectroscopy; Infrared analysis; Infrared spectra; Infrared spectroscopy; Medical imaging; Near infrared radiation; Neural activity; Neuroimaging; pattern classification; signal processing; Spectroscopic analysis; Spectrum analysis; Task analysis
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2023.3315971

We present a dataset for the analysis of human affective states using functional near-infrared spectroscopy (fNIRS). Data were recorded from thirty-one participants who engaged in two tasks. In the emotional perception task the participants passively viewed images sampled from the standard international affective picture system database, which provided ground-truth valence and arousal annotation for the stimuli. In the affective imagery task the participants actively imagined emotional scenarios followed by rating these for subjective valence and arousal. Correlates between the fNIRS signal and the valence-arousal ratings were investigated to estimate the validity of the dataset. Source-code and summaries are provided for a processing pipeline, brain activity group analysis, and estimating baseline classification performance. For classification, prediction experiments are conducted for single-trial 4-class classification of arousal and valence as well as cross-participant classifications, and comparisons between high and low arousal variants of the valence prediction tasks. Finally, classification results are presented for subject-specific and cross-participant models. The dataset is made publicly available to encourage research on affective decoding and downstream applications using fNIRS data.