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An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data
Haptic technologies are becoming increasingly valuable in Human-Computer interaction systems as they provide means of physical interaction with a remote or virtual environment. One of the persistent challenges in tele-haptic systems, communicating haptic information over a computer network, is the s...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9551169/ https://www.ncbi.nlm.nih.gov/pubmed/36237844 http://dx.doi.org/10.3389/frobt.2022.1013043 |
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author | Alsuradi, Haneen Eid, Mohamad |
author_facet | Alsuradi, Haneen Eid, Mohamad |
author_sort | Alsuradi, Haneen |
collection | PubMed |
description | Haptic technologies are becoming increasingly valuable in Human-Computer interaction systems as they provide means of physical interaction with a remote or virtual environment. One of the persistent challenges in tele-haptic systems, communicating haptic information over a computer network, is the synchrony of the delivered haptic information with the rest of the sensory modalities. Delayed haptic feedback can have serious implications on the user performance and overall experience. Limited research efforts have been devoted to studying the implication of haptic delay on the human neural response and relating it to the overall haptic experience. Deep learning could offer autonomous brain activity interpretation in response to a haptic experience such as haptic delay. In this work, we propose an ensemble of 2D CNN and transformer models that is capable of detecting the presence and redseverity of haptic delay from a single-trial Electroencephalography data. Two EEG-based experiments involving visuo-haptic interaction tasks are proposed. The first experiment aims to collect data for detecting the presence of haptic delay during discrete force feedback using a bouncing ball on a racket simulation, while the second aims to collect data for detecting the severity level (none, mild, moderate, severe) of the haptic delay during continuous force feedback via grasping/releasing of an object in a bucket. The ensemble model showed a promising performance with an accuracy of 0.9142 ± 0.0157 for detecting haptic delay during discrete force feedback and 0.6625 ± 0.0067 for classifying the severity of haptic delay during continuous force feedback (4 levels). These results were obtained based on training the model with raw EEG data as well as their wavelet transform using several wavelet kernels. This study is a step forward towards developing cognitive evaluation of the user experience while interaction with haptic interfaces. |
format | Online Article Text |
id | pubmed-9551169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95511692022-10-12 An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data Alsuradi, Haneen Eid, Mohamad Front Robot AI Robotics and AI Haptic technologies are becoming increasingly valuable in Human-Computer interaction systems as they provide means of physical interaction with a remote or virtual environment. One of the persistent challenges in tele-haptic systems, communicating haptic information over a computer network, is the synchrony of the delivered haptic information with the rest of the sensory modalities. Delayed haptic feedback can have serious implications on the user performance and overall experience. Limited research efforts have been devoted to studying the implication of haptic delay on the human neural response and relating it to the overall haptic experience. Deep learning could offer autonomous brain activity interpretation in response to a haptic experience such as haptic delay. In this work, we propose an ensemble of 2D CNN and transformer models that is capable of detecting the presence and redseverity of haptic delay from a single-trial Electroencephalography data. Two EEG-based experiments involving visuo-haptic interaction tasks are proposed. The first experiment aims to collect data for detecting the presence of haptic delay during discrete force feedback using a bouncing ball on a racket simulation, while the second aims to collect data for detecting the severity level (none, mild, moderate, severe) of the haptic delay during continuous force feedback via grasping/releasing of an object in a bucket. The ensemble model showed a promising performance with an accuracy of 0.9142 ± 0.0157 for detecting haptic delay during discrete force feedback and 0.6625 ± 0.0067 for classifying the severity of haptic delay during continuous force feedback (4 levels). These results were obtained based on training the model with raw EEG data as well as their wavelet transform using several wavelet kernels. This study is a step forward towards developing cognitive evaluation of the user experience while interaction with haptic interfaces. Frontiers Media S.A. 2022-09-27 /pmc/articles/PMC9551169/ /pubmed/36237844 http://dx.doi.org/10.3389/frobt.2022.1013043 Text en Copyright © 2022 Alsuradi and Eid. 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 | Robotics and AI Alsuradi, Haneen Eid, Mohamad An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data |
title | An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data |
title_full | An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data |
title_fullStr | An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data |
title_full_unstemmed | An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data |
title_short | An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data |
title_sort | ensemble deep learning approach to evaluate haptic delay from a single trial eeg data |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9551169/ https://www.ncbi.nlm.nih.gov/pubmed/36237844 http://dx.doi.org/10.3389/frobt.2022.1013043 |
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