Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements.

brain closed-loop control electromyogram (EMG) emotion state-space valence wearable

Journal

Frontiers in computational neuroscience
ISSN: 1662-5188
Titre abrégé: Front Comput Neurosci
Pays: Switzerland
ID NLM: 101477956

Informations de publication

Date de publication:
2022
Historique:
received: 27 07 2022
accepted: 21 02 2022
entrez: 11 4 2022
pubmed: 12 4 2022
medline: 12 4 2022
Statut: epublish

Résumé

Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

Identifiants

pubmed: 35399915
doi: 10.3389/fncom.2022.747735
pmc: PMC8990324
doi:

Types de publication

Journal Article

Langues

eng

Pagination

747735

Informations de copyright

Copyright © 2022 Branco, Ehteshami, Azgomi and Faghih.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Luciano R F Branco (LRF)

Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.

Arian Ehteshami (A)

Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.

Hamid Fekri Azgomi (HF)

Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States.

Rose T Faghih (RT)

Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Department of Biomedical Engineering, New York University, New York, NY, United States.

Classifications MeSH