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: [email protected]
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 ALife Robotics Corp. Ltd.
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/).