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Classifying human emotions in HRI: applying global optimization model to EEG brain signals
Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of h...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595007/ https://www.ncbi.nlm.nih.gov/pubmed/37881515 http://dx.doi.org/10.3389/fnbot.2023.1191127 |
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author | Staffa, Mariacarla D'Errico, Lorenzo Sansalone, Simone Alimardani, Maryam |
author_facet | Staffa, Mariacarla D'Errico, Lorenzo Sansalone, Simone Alimardani, Maryam |
author_sort | Staffa, Mariacarla |
collection | PubMed |
description | Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative personality. Using emotion valence and arousal measures derived from frontal brain asymmetry (FBA), several machine learning models were trained to classify human's mental states in response to the robot personality. To improve classification accuracy, all proposed classifiers were subjected to a Global Optimization Model (GOM) based on feature selection and hyperparameter optimization techniques. The results showed that it is possible to classify a user's emotional responses to the robot's behavior from the EEG signals with an accuracy of up to 92%. The outcome of the current study contributes to the first level of the Theory of Mind (ToM) in Human-Robot Interaction, enabling robots to comprehend users' emotional responses and attribute mental states to them. Our work advances the field of social and assistive robotics by paving the way for the development of more empathetic and responsive HRI in the future. |
format | Online Article Text |
id | pubmed-10595007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105950072023-10-25 Classifying human emotions in HRI: applying global optimization model to EEG brain signals Staffa, Mariacarla D'Errico, Lorenzo Sansalone, Simone Alimardani, Maryam Front Neurorobot Neuroscience Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative personality. Using emotion valence and arousal measures derived from frontal brain asymmetry (FBA), several machine learning models were trained to classify human's mental states in response to the robot personality. To improve classification accuracy, all proposed classifiers were subjected to a Global Optimization Model (GOM) based on feature selection and hyperparameter optimization techniques. The results showed that it is possible to classify a user's emotional responses to the robot's behavior from the EEG signals with an accuracy of up to 92%. The outcome of the current study contributes to the first level of the Theory of Mind (ToM) in Human-Robot Interaction, enabling robots to comprehend users' emotional responses and attribute mental states to them. Our work advances the field of social and assistive robotics by paving the way for the development of more empathetic and responsive HRI in the future. Frontiers Media S.A. 2023-10-10 /pmc/articles/PMC10595007/ /pubmed/37881515 http://dx.doi.org/10.3389/fnbot.2023.1191127 Text en Copyright © 2023 Staffa, D'Errico, Sansalone and Alimardani. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Staffa, Mariacarla D'Errico, Lorenzo Sansalone, Simone Alimardani, Maryam Classifying human emotions in HRI: applying global optimization model to EEG brain signals |
title | Classifying human emotions in HRI: applying global optimization model to EEG brain signals |
title_full | Classifying human emotions in HRI: applying global optimization model to EEG brain signals |
title_fullStr | Classifying human emotions in HRI: applying global optimization model to EEG brain signals |
title_full_unstemmed | Classifying human emotions in HRI: applying global optimization model to EEG brain signals |
title_short | Classifying human emotions in HRI: applying global optimization model to EEG brain signals |
title_sort | classifying human emotions in hri: applying global optimization model to eeg brain signals |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595007/ https://www.ncbi.nlm.nih.gov/pubmed/37881515 http://dx.doi.org/10.3389/fnbot.2023.1191127 |
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