Journal of Robotics, Networking and Artificial Life
Vol. 8(3); December (2021), pp. 161–164
DOI: https://doi.org/10.2991/jrnal.k.210922.002; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

A Study for the Realization of Online Magnetoencephalographyusing the Spatio-Spectral Decomposition Algorithms
Kazuhiro Yagi1, Yuta Shibahara2, Lindsey Tate3, Keiko Sakurai3, Hiroki Tamura3,*Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-Nishi, Miyazaki 889-2192, JapanGraduate School of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-Nishi, Miyazaki 889-2192, Japan3
Faculty of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-Nishi, Miyazaki 889-2192, Japan
12ARTICLE INFOABSTRACT

Article History

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 thanMagnetoencephalography (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 clinicalneurofeedback 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 rhythmfrequencies 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 anddiscuss potential strategies and pitfalls for feature selection.

Received 25 November 2020
Accepted 18 June 2021
Keywords
Magnetoencephalography
spatio-spectral decomposition
Morlet wavelet transformneurofeedback© 2021 The Authors. Published by Atlantis Press International B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
For diseases that affect brain function, such as strokes, immediate
treatment with medication and surgery is important, but post-onset
rehabilitation also plays a critical role in the wellbeing of patients.
One of the techniques used for non-invasive brain function evaluation is Magnetoencephalography (MEG) [1,2]. MEG has hightemporal resolution as well as high spatial resolution, and it is commonly used clinically for epilepsy diagnosis and rehabilitation [3].
In the past, electroencephalography was often used for the real time
feedback system [4,5]. Real-time feedback system using MEG has
been reported in the field of Brain–Machine Interface [6]. However,rehabilitation of the real time feedback system using MEG has notbeen established.
However, without the ability to monitor a patient’s relevant brain
activity in real time during rehabilitative exercises, the most efficient rehabilitation cannot occur.
The purpose of this study was to evaluate the use of SpatioSpectral Decomposition (SSD) of real-time MEG data during
spontaneous movement [7–10]. Performance was evaluated bycomparing SSD results to the results from a standard analysis technique, Independent Components Analysis (ICA). SSD completed
Corresponding author. Email: htamura@cc.miyazaki-u.ac.jp*

decomposition faster than did ICA, with the SSD analysis completed about 270 times faster in the preliminary experiment.
Because processing speed is critical to neurofeedback systems, wepresent our investigation of the SSD components and conclude
with suggestions for feature selection.

2. EXPERIMENT
The data were collected using a full-head 306-channel magnetoencephalograph (Vectorview, Elekta-Neuromag, Helsinki, Finland) ata sampling frequency of 1000 [Hz]. To reduce power supply noiseand other interference, measurements were taken inside a magnetic
field shield room [1 (kHz) shielding rate 55.2 (dB)]. The participant was a healthy person who attached an acceleration sensor
to the middle finger of his left hand and performed self-initiatedspontaneous flexion (i.e., bending) and extension (i.e., relaxing)
movements of the indicated finger as shown in Figure 1 (hereinafter referred to as “the task”). The accelerometer data indicated thestart and duration of flexion and extension.

3. ANALYSIS METHOD
After collecting the raw MEG data from all 306 channels, weselected 26 gradiometers corresponding to the right Sensorimotor
Cortex and performed ICA and SSD analyses [11,12], which each

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identified components within the largest cited frequency band forSensorimotor Rhythm (SMR) [13], 8–30 (Hz).
We performed SSD analysis from 40 to 100 s of data collection(the period of task performance). SSD extracted oscillations with
optimized signal-noise ratio (SNR) by maximizing the power from
8 to 30 Hz while simultaneously suppressing the flanking frequency
bins. Then we performed a Morlet wavelet transform on the components in order to examine frequency power over time. As seenin Figure 2, high amplitude artifactual data was recorded between
40 and 50 s. To isolate SSD performance during non-artifact contaminated neural activity and to simplify our preliminary analyses,
we restricted data analysis to the 20 s from 50 to 70 s. There were
10 tasks completed at irregular intervals over this time period.

4. RESULTS
Following the results of topography (Figure 3), we investigated thefirst, second, third, and 10th SSD components (ranked from highest to lowest eigenvalue).

Figure 3 | The 2D topographies of SSD components 1 through 10.

The across-task averaged third and fourth SSD components had
higher SMR band power immediately preceding the task. As shownin Figures 4–7, wavelet analysis was performed on task-lockedaveraged components, indicating average brain activity in the final

Figure 1 | An example of finger extension (i.e., relaxing; left image) andflexion (i.e., bending; right image).

Figure 4 | The Morlet wavelet transform (5–40 Hz) of the task-averaged
first SSD component.

Figure 2 | The Morlet wavelet transform (5–40 Hz) of the first SSD
component from 40 to 100 s.

Figure 5 | The Morlet wavelet transform (5–40 Hz) of the task-averaged
second SSD component.

K. Yagi et al. / Journal of Robotics, Networking and Artificial Life 8(3) 161–164

163second before flexion start. The red line in the figure is the flexion
start point. The third (Figure 6) and 10th (Figure 7) components
each showed a decrease in power around 10–25 Hz immediately
following task execution (Black frame), and this decrease in power
was followed by a subsequent increase in power from around
10–15 Hz (Red frame).

5. CONCLUSIONAs shown in Table 1, the third component had the highest correlation with the accelerometer data, followed by the second and fourthcomponents which had small but likely still relevant correlationswith the accelerometer data.

There are several key points to take away regarding the automationof feature selection in this context. As shown in Table 1, the third
SSD component had the highest correlation with the accelerometer data out of the first five components (i.e., the components withthe strongest eigenvalues or largest SNRs), although this correlation was still under 0.5. The wavelet analysis revealed Event-related
Desynchronization (ERD) and Event-related Synchronization(ERS) in both the third (Figure 6) and 10th (Figure 7) SSD components. Therefore, it is not possible to simply select the first SSDcomponent or the first few SSD components and assume correspondence with the task, nor is it possible to select relevant components based solely on a high-threshold correlation with behavioral
data. From previous study, we have also found that selection of thecomponent with the highest SMR power is ineffective due to contamination by muscular artifacts. This analysis leads us to conclude
that domain-informed feature selection, in this case using ERD andERS characteristics, should be the basis for the ideal neurofeedback system (i.e., a system that allows for maximum flexibility andchange in brain activity during rehabilitation sessions).

Figure 6 | The Morlet wavelet transform (5–40 Hz) of the task-averaged
third SSD component.

In this paper, we performed offline MEG data analysis during
spontaneous flexion and extension of the middle finger of the left
hand to investigate the use of SSD in an online, real-time MEGbased neurofeedback system.

Future work will involve analyzing the correlations between moreof the SSD components (e.g., 6–10) and the accelerometer data atvarious delays. Furthermore, we will investigate the usefulness ofbaseline correction in component selection and of logarithmicallyscaled components as features in unsupervised learning.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

REFERENCES

Figure 7 | The Morlet wavelet transform (5–40 Hz) of the task-averaged
10th SSD component.
Table 1 | The correlations between theaccelerometer data and the first five
SSD components
60 (ms)First SSD
Second SSD
Third SSD
Fourth SSD
Fifth SSD

–0.037
–0.28
0.35
–0.24
–0.13

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AUTHORS INTRODUCTION
Mr. Kazuhiro Yagi

(MEG).

He is a clinical laboratory technician at Junwakai
memorial hospital in Japan. He is currently aDoctoral course student in Interdisciplinary
Graduate School of Agriculture and Engineering,
University of Miyazaki. His main researchinterest is online Magnetoencephalography

Mr. Yuta Shibahara
He received the B.E. degree from Miyazaki
University in 2020. He is currently a Master course
student in Graduate School of Engineering,
University of Miyazaki. His main research interest is online Magnetoencephalography (MEG).

Dr. Lindsey Tate
She received a B.A. in Psychology(Neuroscience) from St. Edward’s University
in Austin, TX, USA in 2013. In 2016 and 2018,
respectively, she received her M.S. and PhD in
Cognitive Psychology from the University ofOklahoma, where she conducted perceptualand behavioral neuroscience research using EEG and MEG.
Now she works as a researcher in the Faculty of Engineering
at the University of Miyazaki. Her main research interests areapplying machine learning and other algorithms to real-time
non-invasive physiological data for use in clinical and nonclinical biofeedback systems.

Dr. Keiko Sakurai
She received B.E. and M.E. degrees from Kyushu
Institute of Technology in 2005 and 2007. From2007 to 2010, she was in charge of the designof the drive for PC at Pioneer Corporation inJapan. She was engaged in research on both
high noise reduction and energy saving. She
graduated Doctor course at Interdisciplinary
Graduate School of Agriculture and Engineering in Universityof Miyazaki. Now she works as a researcher in Faculty ofEngineering at University of Miyazaki. Her main research interests are biological signal measurement systems, especially gazeestimation system.

Prof. Hiroki Tamura
He received the B.E. and M.E. degree fromMiyazaki University in 1998 and 2000,
respectively. From 2000 to 2001, he was an
Engineer in Asahi Kasei Corporation, Japan.
In 2001, he joined Toyama University, Toyama,
Japan, where he was a Technical Official in
the Department of Intellectual InformationSystems. In 2006, he joined Miyazaki University, Miyazaki,Japan, where he was an Assistant Professor in the Departmentof Electrical and Electronic Engineering. Since 2015, he iscurrently a Professor in the Department of Environmental
Robotics. His main research interests are Neural Networks andOptimization Problems. In recent years, he has had interest in
Biomedical Signal Processing using Soft Computing.