Cargando…

Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games

In this paper we present a method to examine the synchrony between brains without the need to carry out simultaneous recordings of EEG signals from two people which is the essence of hyper-scanning studies. We used anonymous random walks to spatially encode the entire graph structure without relying...

Descripción completa

Detalles Bibliográficos
Autores principales: Zuckerman, Inon, Mizrahi, Dor, Laufer, Ilan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358924/
https://www.ncbi.nlm.nih.gov/pubmed/37471403
http://dx.doi.org/10.1371/journal.pone.0288822
_version_ 1785075771251884032
author Zuckerman, Inon
Mizrahi, Dor
Laufer, Ilan
author_facet Zuckerman, Inon
Mizrahi, Dor
Laufer, Ilan
author_sort Zuckerman, Inon
collection PubMed
description In this paper we present a method to examine the synchrony between brains without the need to carry out simultaneous recordings of EEG signals from two people which is the essence of hyper-scanning studies. We used anonymous random walks to spatially encode the entire graph structure without relying on data at the level of individual nodes. Anonymous random walks enabled us to encapsulate the structure of a graph regardless of the specific node labels. That is, random walks that visited different nodes in the same sequence resulted in the same anonymous walk encoding. We have analyzed the EEG data offline and matched each possible pair of players from the entire pool of players that performed a series of tacit coordination games. Specifically, we compared between two network patterns associated with each possible pair of players. By using classification performed on the spatial distance between each pair of individual brain patterns, analyzed by the random walk algorithm, we tried to predict whether each possible pair of players has managed to converge on the same solution in each tacit coordination game. Specifically, the distance between a pair of vector embeddings, each associated with one of the players, was used as input for a classification model for the purpose of predicting whether the two corresponding players have managed to achieve successful coordination. Our model reached a classification accuracy of ~85%.
format Online
Article
Text
id pubmed-10358924
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-103589242023-07-21 Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games Zuckerman, Inon Mizrahi, Dor Laufer, Ilan PLoS One Research Article In this paper we present a method to examine the synchrony between brains without the need to carry out simultaneous recordings of EEG signals from two people which is the essence of hyper-scanning studies. We used anonymous random walks to spatially encode the entire graph structure without relying on data at the level of individual nodes. Anonymous random walks enabled us to encapsulate the structure of a graph regardless of the specific node labels. That is, random walks that visited different nodes in the same sequence resulted in the same anonymous walk encoding. We have analyzed the EEG data offline and matched each possible pair of players from the entire pool of players that performed a series of tacit coordination games. Specifically, we compared between two network patterns associated with each possible pair of players. By using classification performed on the spatial distance between each pair of individual brain patterns, analyzed by the random walk algorithm, we tried to predict whether each possible pair of players has managed to converge on the same solution in each tacit coordination game. Specifically, the distance between a pair of vector embeddings, each associated with one of the players, was used as input for a classification model for the purpose of predicting whether the two corresponding players have managed to achieve successful coordination. Our model reached a classification accuracy of ~85%. Public Library of Science 2023-07-20 /pmc/articles/PMC10358924/ /pubmed/37471403 http://dx.doi.org/10.1371/journal.pone.0288822 Text en © 2023 Zuckerman et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zuckerman, Inon
Mizrahi, Dor
Laufer, Ilan
Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games
title Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games
title_full Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games
title_fullStr Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games
title_full_unstemmed Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games
title_short Offline EEG hyper-scanning using anonymous walk embeddings in tacit coordination games
title_sort offline eeg hyper-scanning using anonymous walk embeddings in tacit coordination games
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358924/
https://www.ncbi.nlm.nih.gov/pubmed/37471403
http://dx.doi.org/10.1371/journal.pone.0288822
work_keys_str_mv AT zuckermaninon offlineeeghyperscanningusinganonymouswalkembeddingsintacitcoordinationgames
AT mizrahidor offlineeeghyperscanningusinganonymouswalkembeddingsintacitcoordinationgames
AT lauferilan offlineeeghyperscanningusinganonymouswalkembeddingsintacitcoordinationgames