A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms

Neurofeedback systems have been found to be effective in the clinical rehabilitation of paralysis. However, most systems exist only for use with electroencephalography, which is cumbersome to apply to patients and has lower spatial resolution than Magnetoencephalography (MEG). Furthermore, the best...

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Autores principales:	Kazuhiro Yagi, Yuta Shibahara, Lindsey Tate, Keiko Sakurai, Hiroki Tamura
Formato:	article
Lenguaje:	EN
Publicado:	Atlantis Press 2021
Materias:	Magnetoencephalography spatio-spectral decomposition Morlet wavelet transform neurofeedback Technology T
Acceso en línea:	https://doaj.org/article/ce0062fc9a794b51aead805249c06637
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spelling	oai:doaj.org-article:ce0062fc9a794b51aead805249c066372021-11-17T08:30:13ZA Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms10.2991/jrnal.k.210922.0021259613832352-6386https://doaj.org/article/ce0062fc9a794b51aead805249c066372021-10-01T00:00:00Zhttps://www.atlantis-press.com/article/125961383/viewhttps://doaj.org/toc/2352-6386Neurofeedback systems have been found to be effective in the clinical rehabilitation of paralysis. However, most systems exist only for use with electroencephalography, which is cumbersome to apply to patients and has lower spatial resolution than Magnetoencephalography (MEG). Furthermore, the best practices for neural data feature extraction and feature selection are not well established. The inclusion of the best performing feature extraction algorithms is critical to the development of clinical neurofeedback systems. Using simultaneously collected MEG and accelerometer data before and during 10 spontaneous finger movements, we performed an in-depth comparison of the Spatio-Spectral Decomposition (SSD) algorithms for their individual abilities to isolate movement-relevant features in brain activity. Having restricted raw data to that from sensorimotor rhythm frequencies in select MEG sensors over sensorimotor cortex, we compared SSD components using: (1) 2D topographies, (2) activations over time, (3) and correlations with accelerometer data at both 0 and 60 ms time delays. We will discuss these results and suggestions for application to neurofeedback systems. In particular, we will present detailed visualizations of SSD results and discuss potential strategies and pitfalls for feature selection.Kazuhiro YagiYuta ShibaharaLindsey TateKeiko SakuraiHiroki TamuraAtlantis PressarticleMagnetoencephalographyspatio-spectral decompositionMorlet wavelet transformneurofeedbackTechnologyTENJournal of Robotics, Networking and Artificial Life (JRNAL), Vol 8, Iss 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Magnetoencephalography spatio-spectral decomposition Morlet wavelet transform neurofeedback Technology T
spellingShingle	Magnetoencephalography spatio-spectral decomposition Morlet wavelet transform neurofeedback Technology T Kazuhiro Yagi Yuta Shibahara Lindsey Tate Keiko Sakurai Hiroki Tamura A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms
description	Neurofeedback systems have been found to be effective in the clinical rehabilitation of paralysis. However, most systems exist only for use with electroencephalography, which is cumbersome to apply to patients and has lower spatial resolution than Magnetoencephalography (MEG). Furthermore, the best practices for neural data feature extraction and feature selection are not well established. The inclusion of the best performing feature extraction algorithms is critical to the development of clinical neurofeedback systems. Using simultaneously collected MEG and accelerometer data before and during 10 spontaneous finger movements, we performed an in-depth comparison of the Spatio-Spectral Decomposition (SSD) algorithms for their individual abilities to isolate movement-relevant features in brain activity. Having restricted raw data to that from sensorimotor rhythm frequencies in select MEG sensors over sensorimotor cortex, we compared SSD components using: (1) 2D topographies, (2) activations over time, (3) and correlations with accelerometer data at both 0 and 60 ms time delays. We will discuss these results and suggestions for application to neurofeedback systems. In particular, we will present detailed visualizations of SSD results and discuss potential strategies and pitfalls for feature selection.
format	article
author	Kazuhiro Yagi Yuta Shibahara Lindsey Tate Keiko Sakurai Hiroki Tamura
author_facet	Kazuhiro Yagi Yuta Shibahara Lindsey Tate Keiko Sakurai Hiroki Tamura
author_sort	Kazuhiro Yagi
title	A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms
title_short	A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms
title_full	A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms
title_fullStr	A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms
title_full_unstemmed	A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms
title_sort	study for the realization of online magnetoencephalography using the spatio-spectral decomposition algorithms
publisher	Atlantis Press
publishDate	2021
url	https://doaj.org/article/ce0062fc9a794b51aead805249c06637
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A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms

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