TY - JOUR

T1 - NEMO

T2 - A Database for Emotion Analysis Using Functional Near-Infrared Spectroscopy

AU - Spape, Michiel

AU - Makela, Kalle

AU - Ruotsalo, Tuukka

N1 - Publisher Copyright: IEEE

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Affective computing

KW - Biomedical monitoring

KW - Databases

KW - Electroencephalography

KW - emotion classification

KW - FNIRS

KW - Functional near-infrared spectroscopy

KW - functional near-infrared spectroscopy

KW - Neural activity

KW - Neuroimaging

KW - pattern classification

KW - signal processing

KW - Task analysis

UR - http://www.scopus.com/inward/record.url?scp=85174852109&partnerID=8YFLogxK

U2 - 10.1109/TAFFC.2023.3315971

DO - 10.1109/TAFFC.2023.3315971

M3 - Journal article

AN - SCOPUS:85174852109

JO - IEEE Transactions on Affective Computing

JF - IEEE Transactions on Affective Computing

SN - 1949-3045

ER -