Journal of Robotics, Networking and Artificial Life

Volume 8, Issue 3, December 2021, Pages 161 - 164

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

Authors
Kazuhiro Yagi1, Yuta Shibahara2, Lindsey Tate3, Keiko Sakurai3, Hiroki Tamura3, *
1Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-Nishi, Miyazaki 889-2192, Japan
2Graduate School of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-Nishi, Miyazaki 889-2192, Japan
3Faculty of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-Nishi, Miyazaki 889-2192, Japan
*Corresponding author. Email: htamura@cc.miyazaki-u.ac.jp
Corresponding Author
Hiroki Tamura
Received 25 November 2020, Accepted 18 June 2021, Available Online 9 October 2021.
DOI
https://doi.org/10.2991/jrnal.k.210922.002How to use a DOI?
Keywords
Magnetoencephalography; spatio-spectral decomposition; Morlet wavelet transform; neurofeedback
Abstract

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.

Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
8 - 3
Pages
161 - 164
Publication Date
2021/10/09
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.k.210922.002How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Kazuhiro Yagi
AU  - Yuta Shibahara
AU  - Lindsey Tate
AU  - Keiko Sakurai
AU  - Hiroki Tamura
PY  - 2021
DA  - 2021/10/09
TI  - A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 161
EP  - 164
VL  - 8
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.210922.002
DO  - https://doi.org/10.2991/jrnal.k.210922.002
ID  - Yagi2021
ER  -