Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity

Shohei Tsuchimoto; Shuka Shibusawa; Seitaro Iwama; Masaaki Hayashi; Kohei Okuyama; Nobuaki Mizuguchi; Kenji Kato; Junichi Ushiba

doi:10.1016/j.jneumeth.2021.109089

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity

J Neurosci Methods. 2021 Apr 1:353:109089. doi: 10.1016/j.jneumeth.2021.109089. Epub 2021 Jan 27.

Authors

Shohei Tsuchimoto¹, Shuka Shibusawa², Seitaro Iwama³, Masaaki Hayashi³, Kohei Okuyama⁴, Nobuaki Mizuguchi⁵, Kenji Kato⁶, Junichi Ushiba⁷

Affiliations

¹ School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, 223-8522, Japan; Division of System Neuroscience, National Institute for Physiological Sciences, Aichi, 444-8585, Japan.
² Graduate School of Frontier Biosciences, Osaka University, Osaka, 565-0871, Japan; National Institute of Information and Communications Technology, Center for Information and Neural Networks, Osaka, 565-0871, Japan; Japan Society for the Promotion of Science, Tokyo, 102-0083, Japan.
³ School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, 223-8522, Japan.
⁴ Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan.
⁵ School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, 223-8522, Japan; Research Organization of Science and Technology, Ritsumeikan University, Shiga, 525-8577, Japan.
⁶ Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology, Aichi, 474-8511, Japan.
⁷ Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan. Electronic address: ushiba@bio.keio.ac.jp.

PMID: 33508408
DOI: 10.1016/j.jneumeth.2021.109089

Abstract

Background: Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters.

New method: We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter.

Results: The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal.

Comparison with existing methods: Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR.

Conclusions: Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.

Keywords: EEG sensorimotor rhythm; EEG-fMRI simultaneous recording; Mu rhythm; Resting-state; Sensorimotor activity.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain Mapping
Electroencephalography*
Humans
Magnetic Resonance Imaging
Sensorimotor Cortex*
Signal-To-Noise Ratio