Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction

Masaaki Hayashi, Shohei Tsuchimoto, Nobuaki Mizuguchi, Mizuki Miyatake, Shoko Kasuga, Junichi Ushiba

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: A critical feature for the maintenance of precise skeletal muscle force production by the human brain is its ability to configure motor function activity dynamically and adaptively in response to visual and somatosensory information. Existing studies have concluded that not only the sensorimotor area but also distributed cortical areas act cooperatively in the generation of motor commands for voluntary force production to the desired level. However, less attention has been paid to such physiological mechanisms in conventional brain-computer interface (BCI) design and implementation. We proposed a new, physiologically inspired two-stage decoding method to see its contribution on accuracy improvement of BCI. APPROACH: We performed whole-head high-density scalp electroencephalographic (EEG) recording during a right finger force-matching task at three strength levels (20%, 40%, and 60% maximal voluntary contraction following a resting state). A two-stage regression approach was employed that decodes muscle contraction level from EEG signals in the multi-level force-matching task and translates them into: (1) presence/absence of muscle contraction as a first stage; and (2) muscle contraction level as a second stage. Dimensionality reduction of the EEG signals, using principal component analysis, avoided multicollinearity during multiple regression, and data-driven stepwise multiple regression identified EEG components that were involved in the multi-level force-matching task. MAIN RESULTS: An alternatively tuned two-stage regressor accurately decoded muscle contraction level with online processing rather than the conventional decoders, and identified EEG components that were related to voluntary force production. Relaxation/contraction state-dependent EEG components were localized dominantly in the contralateral parieto-temporal regions, whereas multi-level force regulation-dependent EEG components came from the fronto-parietal regions. SIGNIFICANCE: Our findings identify respective cortical signalings during relaxation/contraction and multi-level force regulation using a sensor-based approach with EEG. Simulation-based assessment of the current physiologically inspired decoding technique proved improved accuracy in online BCI control.

Original languageEnglish
Number of pages1
JournalJournal of neural engineering
Volume16
Issue number5
DOIs
Publication statusPublished - 2019 Aug 21

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Muscle Contraction
Electroencephalography
Scalp
Brain-Computer Interfaces
Muscle
Advisory Committees
Brain computer interface
Parietal Lobe
Aptitude
Temporal Lobe
Principal Component Analysis
Decoding
Fingers
Skeletal Muscle
Motor Activity
Head
Maintenance
Brain
Principal component analysis
Sensors

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction. / Hayashi, Masaaki; Tsuchimoto, Shohei; Mizuguchi, Nobuaki; Miyatake, Mizuki; Kasuga, Shoko; Ushiba, Junichi.

In: Journal of neural engineering, Vol. 16, No. 5, 21.08.2019.

Research output: Contribution to journalArticle

Hayashi, Masaaki ; Tsuchimoto, Shohei ; Mizuguchi, Nobuaki ; Miyatake, Mizuki ; Kasuga, Shoko ; Ushiba, Junichi. / Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction. In: Journal of neural engineering. 2019 ; Vol. 16, No. 5.
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abstract = "OBJECTIVE: A critical feature for the maintenance of precise skeletal muscle force production by the human brain is its ability to configure motor function activity dynamically and adaptively in response to visual and somatosensory information. Existing studies have concluded that not only the sensorimotor area but also distributed cortical areas act cooperatively in the generation of motor commands for voluntary force production to the desired level. However, less attention has been paid to such physiological mechanisms in conventional brain-computer interface (BCI) design and implementation. We proposed a new, physiologically inspired two-stage decoding method to see its contribution on accuracy improvement of BCI. APPROACH: We performed whole-head high-density scalp electroencephalographic (EEG) recording during a right finger force-matching task at three strength levels (20{\%}, 40{\%}, and 60{\%} maximal voluntary contraction following a resting state). A two-stage regression approach was employed that decodes muscle contraction level from EEG signals in the multi-level force-matching task and translates them into: (1) presence/absence of muscle contraction as a first stage; and (2) muscle contraction level as a second stage. Dimensionality reduction of the EEG signals, using principal component analysis, avoided multicollinearity during multiple regression, and data-driven stepwise multiple regression identified EEG components that were involved in the multi-level force-matching task. MAIN RESULTS: An alternatively tuned two-stage regressor accurately decoded muscle contraction level with online processing rather than the conventional decoders, and identified EEG components that were related to voluntary force production. Relaxation/contraction state-dependent EEG components were localized dominantly in the contralateral parieto-temporal regions, whereas multi-level force regulation-dependent EEG components came from the fronto-parietal regions. SIGNIFICANCE: Our findings identify respective cortical signalings during relaxation/contraction and multi-level force regulation using a sensor-based approach with EEG. Simulation-based assessment of the current physiologically inspired decoding technique proved improved accuracy in online BCI control.",
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