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MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer

Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated fr...

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Autores principales: Ovchinnikova, Anastasia O., Vasilyev, Anatoly N., Zubarev, Ivan P., Kozyrskiy, Bogdan L., Shishkin, Sergei L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892913/
https://www.ncbi.nlm.nih.gov/pubmed/33613182
http://dx.doi.org/10.3389/fnins.2021.619591
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author Ovchinnikova, Anastasia O.
Vasilyev, Anatoly N.
Zubarev, Ivan P.
Kozyrskiy, Bogdan L.
Shishkin, Sergei L.
author_facet Ovchinnikova, Anastasia O.
Vasilyev, Anatoly N.
Zubarev, Ivan P.
Kozyrskiy, Bogdan L.
Shishkin, Sergei L.
author_sort Ovchinnikova, Anastasia O.
collection PubMed
description Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated from spontaneous ones using short segments of magnetoencephalography (MEG) data measured immediately after the fixation onset. Recently proposed convolutional neural networks (CNNs), linear finite impulse response filters CNN (LF-CNN) and vector autoregressive CNN (VAR-CNN), were applied for binary classification of the MEG signals related to spontaneous and voluntary eye fixations collected in healthy participants (n = 25) who performed a game-like task by fixating on targets voluntarily for 500 ms or longer. Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0.66 ± 0.07 for LF-CNN and 0.67 ± 0.07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0.91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis. Current results for intention detection prior to gaze-based interface’s feedback, however, are not sufficient for online single-trial eye fixation classification using MEG data alone, and further work is needed to find out if it could be used in practical applications.
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spelling pubmed-78929132021-02-20 MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer Ovchinnikova, Anastasia O. Vasilyev, Anatoly N. Zubarev, Ivan P. Kozyrskiy, Bogdan L. Shishkin, Sergei L. Front Neurosci Neuroscience Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated from spontaneous ones using short segments of magnetoencephalography (MEG) data measured immediately after the fixation onset. Recently proposed convolutional neural networks (CNNs), linear finite impulse response filters CNN (LF-CNN) and vector autoregressive CNN (VAR-CNN), were applied for binary classification of the MEG signals related to spontaneous and voluntary eye fixations collected in healthy participants (n = 25) who performed a game-like task by fixating on targets voluntarily for 500 ms or longer. Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0.66 ± 0.07 for LF-CNN and 0.67 ± 0.07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0.91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis. Current results for intention detection prior to gaze-based interface’s feedback, however, are not sufficient for online single-trial eye fixation classification using MEG data alone, and further work is needed to find out if it could be used in practical applications. Frontiers Media S.A. 2021-02-05 /pmc/articles/PMC7892913/ /pubmed/33613182 http://dx.doi.org/10.3389/fnins.2021.619591 Text en Copyright © 2021 Ovchinnikova, Vasilyev, Zubarev, Kozyrskiy and Shishkin. http://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
Ovchinnikova, Anastasia O.
Vasilyev, Anatoly N.
Zubarev, Ivan P.
Kozyrskiy, Bogdan L.
Shishkin, Sergei L.
MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer
title MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer
title_full MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer
title_fullStr MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer
title_full_unstemmed MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer
title_short MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer
title_sort meg-based detection of voluntary eye fixations used to control a computer
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892913/
https://www.ncbi.nlm.nih.gov/pubmed/33613182
http://dx.doi.org/10.3389/fnins.2021.619591
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