TY - JOUR
T1 - Building predictive models of emotion with functional near-infrared spectroscopy
AU - Bandara, Danushka
AU - Velipasalar, Senem
AU - Bratt, Sarah
AU - Hirshfield, Leanne
N1 - Funding Information:
This work has been funded in part by National Science Foundation (NSF) under CAREER grant CNS-1206291 and NSF Grant CNS-1302559. We would also like to thank the Air Force Office of Sponsored Research (award FA9550-15-1-0021) for their support of this research.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/2
Y1 - 2018/2
N2 - We demonstrate the capability of discriminating between affective states on the valence and arousal dimensions using functional near-infrared spectroscopy (fNIRS), a practical non-invasive device that benefits from its ability to localize activation in functional brain regions with spatial resolution superior to the Electroencephalograph (EEG). The high spatial resolution of fNIRS enables us to identify the neural correlates of emotion with spatial precision comparable to fMRI, but without requiring the use of the constricting and impractical fMRI scanner. We make these predictions across subjects, creating the capacity to generalize the model to new participants. We designed the experiment and evaluated our results in the context of a prior experiment—based on the same basic protocol and stimulus materials—which used EEG to measure participants’ valence and arousal. The F1-scores achieved by our classifiers suggest that fNIRS is particularly useful at distinguishing between high and low levels of valence (F1-score of 0.739), which has proven to be difficult to measure with physiological sensors.
AB - We demonstrate the capability of discriminating between affective states on the valence and arousal dimensions using functional near-infrared spectroscopy (fNIRS), a practical non-invasive device that benefits from its ability to localize activation in functional brain regions with spatial resolution superior to the Electroencephalograph (EEG). The high spatial resolution of fNIRS enables us to identify the neural correlates of emotion with spatial precision comparable to fMRI, but without requiring the use of the constricting and impractical fMRI scanner. We make these predictions across subjects, creating the capacity to generalize the model to new participants. We designed the experiment and evaluated our results in the context of a prior experiment—based on the same basic protocol and stimulus materials—which used EEG to measure participants’ valence and arousal. The F1-scores achieved by our classifiers suggest that fNIRS is particularly useful at distinguishing between high and low levels of valence (F1-score of 0.739), which has proven to be difficult to measure with physiological sensors.
KW - Affective computing
KW - Arousal classification
KW - Brain signal processing
KW - Emotion classification
KW - Valence classification
KW - fNIRS
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U2 - 10.1016/j.ijhcs.2017.10.001
DO - 10.1016/j.ijhcs.2017.10.001
M3 - Article
AN - SCOPUS:85032677223
SN - 1071-5819
VL - 110
SP - 75
EP - 85
JO - International Journal of Human Computer Studies
JF - International Journal of Human Computer Studies
ER -