Abstract
This paper presents two feature extraction methods for training different classification models for the detection of pleasure and displeasure defined by high and low valence levels using functional near-infrared spectroscopy (fNIRS). The study involved fifty-four volunteers who were presented with emotion-inducing image blocks while their prefrontal cortex brain activity was recorded by an fNIRS device. Results from the participants’ responses to a questionnaire showed no significant differences in valence-related scores. The study used statistical and ROCKET methods to extract features and trained six models to discriminate high and low valence. The results showed that ROCKET features performed best with kNN, MLP and SVM classifiers. The research highlighted the potential of different feature extraction methods to improve the accuracy of biosignal analysis from fNIRS devices.
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Acknowledgements
Grants PID2020-115220RB-C21 and EQC2019-006063-P funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way to make Europe”. Grant 2022-GRIN-34436 funded by Universidad de Castilla-La Mancha and by “ERDF A way of making Europe”. Grant PTA2019-016876-I funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. This research was also supported by CIBERSAM, Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación.
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Sánchez-Reolid, D., Sánchez-Reolid, R., Fernández-Caballero, A., Borja, A.L. (2023). Pleasure and Displeasure Identification from fNIRS Signals. In: Novais, P., et al. Ambient Intelligence – Software and Applications – 14th International Symposium on Ambient Intelligence. ISAmI 2023. Lecture Notes in Networks and Systems, vol 770. Springer, Cham. https://doi.org/10.1007/978-3-031-43461-7_21
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