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Switch or stay? Automatic classification of internal mental states in bistable perception

The human brain goes through numerous cognitive states, most of these being hidden or implicit while performing a task, and understanding them is of great practical importance. However, identifying internal mental states is quite challenging as these states are difficult to label, usually short-live...

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Autores principales: Sen, Susmita, Daimi, Syed Naser, Watanabe, Katsumi, Takahashi, Kohske, Bhattacharya, Joydeep, Saha, Goutam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6973829/
https://www.ncbi.nlm.nih.gov/pubmed/32015769
http://dx.doi.org/10.1007/s11571-019-09548-7
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author Sen, Susmita
Daimi, Syed Naser
Watanabe, Katsumi
Takahashi, Kohske
Bhattacharya, Joydeep
Saha, Goutam
author_facet Sen, Susmita
Daimi, Syed Naser
Watanabe, Katsumi
Takahashi, Kohske
Bhattacharya, Joydeep
Saha, Goutam
author_sort Sen, Susmita
collection PubMed
description The human brain goes through numerous cognitive states, most of these being hidden or implicit while performing a task, and understanding them is of great practical importance. However, identifying internal mental states is quite challenging as these states are difficult to label, usually short-lived, and generally, overlap with other tasks. One such problem pertains to bistable perception, which we consider to consist of two internal mental states, namely, transition and maintenance. The transition state is short-lived and represents a change in perception while the maintenance state is comparatively longer and represents a stable perception. In this study, we proposed a novel approach for characterizing the duration of transition and maintenance states and classified them from the neuromagnetic brain responses. Participants were presented with various types of ambiguous visual stimuli on which they indicated the moments of perceptual switches, while their magnetoencephalogram (MEG) data were recorded. We extracted different spatio-temporal features based on wavelet transform, and classified transition and maintenance states on a trial-by-trial basis. We obtained a classification accuracy of 79.58% and 78.40% using SVM and ANN classifiers, respectively. Next, we investigated the temporal fluctuations of these internal mental representations as captured by our classifier model and found that the accuracy showed a decreasing trend as the maintenance state was moved towards the next transition state. Further, to identify the neural sources corresponding to these internal mental states, we performed source analysis on MEG signals. We observed the involvement of sources from the parietal lobe, occipital lobe, and cerebellum in distinguishing transition and maintenance states. Cross-conditional classification analysis established generalization potential of wavelet features. Altogether, this study presents an automatic classification of endogenous mental states involved in bistable perception by establishing brain-behavior relationships at the single-trial level.
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spelling pubmed-69738292020-02-03 Switch or stay? Automatic classification of internal mental states in bistable perception Sen, Susmita Daimi, Syed Naser Watanabe, Katsumi Takahashi, Kohske Bhattacharya, Joydeep Saha, Goutam Cogn Neurodyn Research Article The human brain goes through numerous cognitive states, most of these being hidden or implicit while performing a task, and understanding them is of great practical importance. However, identifying internal mental states is quite challenging as these states are difficult to label, usually short-lived, and generally, overlap with other tasks. One such problem pertains to bistable perception, which we consider to consist of two internal mental states, namely, transition and maintenance. The transition state is short-lived and represents a change in perception while the maintenance state is comparatively longer and represents a stable perception. In this study, we proposed a novel approach for characterizing the duration of transition and maintenance states and classified them from the neuromagnetic brain responses. Participants were presented with various types of ambiguous visual stimuli on which they indicated the moments of perceptual switches, while their magnetoencephalogram (MEG) data were recorded. We extracted different spatio-temporal features based on wavelet transform, and classified transition and maintenance states on a trial-by-trial basis. We obtained a classification accuracy of 79.58% and 78.40% using SVM and ANN classifiers, respectively. Next, we investigated the temporal fluctuations of these internal mental representations as captured by our classifier model and found that the accuracy showed a decreasing trend as the maintenance state was moved towards the next transition state. Further, to identify the neural sources corresponding to these internal mental states, we performed source analysis on MEG signals. We observed the involvement of sources from the parietal lobe, occipital lobe, and cerebellum in distinguishing transition and maintenance states. Cross-conditional classification analysis established generalization potential of wavelet features. Altogether, this study presents an automatic classification of endogenous mental states involved in bistable perception by establishing brain-behavior relationships at the single-trial level. Springer Netherlands 2019-07-19 2020-02 /pmc/articles/PMC6973829/ /pubmed/32015769 http://dx.doi.org/10.1007/s11571-019-09548-7 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research Article
Sen, Susmita
Daimi, Syed Naser
Watanabe, Katsumi
Takahashi, Kohske
Bhattacharya, Joydeep
Saha, Goutam
Switch or stay? Automatic classification of internal mental states in bistable perception
title Switch or stay? Automatic classification of internal mental states in bistable perception
title_full Switch or stay? Automatic classification of internal mental states in bistable perception
title_fullStr Switch or stay? Automatic classification of internal mental states in bistable perception
title_full_unstemmed Switch or stay? Automatic classification of internal mental states in bistable perception
title_short Switch or stay? Automatic classification of internal mental states in bistable perception
title_sort switch or stay? automatic classification of internal mental states in bistable perception
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6973829/
https://www.ncbi.nlm.nih.gov/pubmed/32015769
http://dx.doi.org/10.1007/s11571-019-09548-7
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