Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study

ObjectiveThis study aims to investigate the impact of stimulus environments (Virtual Theatre vs Monitor) and error probabilities (20% vs 50%) on attentional states, Error Potentials (ErrP), and machine learning classification performance.ApproachEEG signals were recorded using different protocols, a...

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Main Authors: Andrea Farabbi, Luca Mainardi
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Virtual Reality
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/frvir.2024.1433082/full
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author Andrea Farabbi
Luca Mainardi
author_facet Andrea Farabbi
Luca Mainardi
author_sort Andrea Farabbi
collection DOAJ
description ObjectiveThis study aims to investigate the impact of stimulus environments (Virtual Theatre vs Monitor) and error probabilities (20% vs 50%) on attentional states, Error Potentials (ErrP), and machine learning classification performance.ApproachEEG signals were recorded using different protocols, and features were extracted for subsequent analysis from single-trial response and attention level was computed from the second preceding error processing stimulation.ResultsThe results indicate significant differences across conditions: the Monitor environment consistently elicited higher and faster ErrP responses and elevated attentional states compared to Virtual Theatre. Additionally, classification performance in the Monitor environment outperformed Virtual Theatre consistently. Further analysis revealed that the 20% error probability protocol yielded increased ErrP responses, heightened attentional states, and superior classification performance compared to the 50% protocol. Classification performance under the 20% error probability condition consistently exceeded 75% validation and test sets. Moreover, a significant correlation between attention-related features and ErrP characteristics was observed, highlighting the intricate relationship between error processing and attentional engagement.RelevanceThese findings underscore the importance of considering stimulus environments and error probabilities in cognitive neuroscience research and machine learning applications. Understanding these factors can inform experimental design and model development, ultimately advancing our comprehension of cognitive processes and enhancing real-world applications of machine learning algorithms.
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spelling doaj-art-3b905ffa3264468e84bd5cf92a00d4412025-08-20T01:58:16ZengFrontiers Media S.A.Frontiers in Virtual Reality2673-41922024-12-01510.3389/frvir.2024.14330821433082Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative studyAndrea FarabbiLuca MainardiObjectiveThis study aims to investigate the impact of stimulus environments (Virtual Theatre vs Monitor) and error probabilities (20% vs 50%) on attentional states, Error Potentials (ErrP), and machine learning classification performance.ApproachEEG signals were recorded using different protocols, and features were extracted for subsequent analysis from single-trial response and attention level was computed from the second preceding error processing stimulation.ResultsThe results indicate significant differences across conditions: the Monitor environment consistently elicited higher and faster ErrP responses and elevated attentional states compared to Virtual Theatre. Additionally, classification performance in the Monitor environment outperformed Virtual Theatre consistently. Further analysis revealed that the 20% error probability protocol yielded increased ErrP responses, heightened attentional states, and superior classification performance compared to the 50% protocol. Classification performance under the 20% error probability condition consistently exceeded 75% validation and test sets. Moreover, a significant correlation between attention-related features and ErrP characteristics was observed, highlighting the intricate relationship between error processing and attentional engagement.RelevanceThese findings underscore the importance of considering stimulus environments and error probabilities in cognitive neuroscience research and machine learning applications. Understanding these factors can inform experimental design and model development, ultimately advancing our comprehension of cognitive processes and enhancing real-world applications of machine learning algorithms.https://www.frontiersin.org/articles/10.3389/frvir.2024.1433082/fullbrain computer interfaceselectroencephalographyerror related potentialsignal processingvirtual realitymachine learning
spellingShingle Andrea Farabbi
Luca Mainardi
Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study
Frontiers in Virtual Reality
brain computer interfaces
electroencephalography
error related potential
signal processing
virtual reality
machine learning
title Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study
title_full Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study
title_fullStr Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study
title_full_unstemmed Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study
title_short Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study
title_sort assessing the impact of stimulation environment and error probability on errp eeg response detection and subject attention an explorative study
topic brain computer interfaces
electroencephalography
error related potential
signal processing
virtual reality
machine learning
url https://www.frontiersin.org/articles/10.3389/frvir.2024.1433082/full
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