Expression of kinematic information in eeg and meg signals during visuomotor tasks

Reinmar Kobler, Andreea Ioana Sburlea, Valeria Mondini, Elizaveta Kolesnichenko , Hiroaki Hashimoto, Masayuki Hirata, Gernot Müller-Putz

Publikation: KonferenzbeitragAbstractForschungBegutachtung

Abstract

Electroencephalographic (EEG) and magnetoencephalographic (MEG) signals carry information about upper-limb kinematics. Their expression in M/EEG signals is potentially influenced by many factors. We believe that vision and movement type (i.e., discrete vs continuous) are key factors. To identify their effects, we investigated visuomotor tasks in several studies.
In a first EEG study (15 healthy subjects), we investigated the influence of vision during goal-directed upper-limb movements [1]. Our paradigm implemented a center-out task (COT) followed by a pursuit tracking task (PTT). It involved two visual stimuli (target and cursor) in two conditions. In one condition (exe), subjects controlled the cursor with their right hand. In the second condition (obs), subjects observed a computer controlled cursor. Regarding the PTT, our findings indicate that premotor and primary sensorimotor areas carried significant information about cursor velocity in the exe condition, whereas parieto-occipital areas carried significant directional information in either condition. Regarding the COT, we could classify condition (exe vs obs) and direction (left, up, right and down) above significance level. The two classifiers relied on different cortical areas. Contra-lateral pre- and primary motor areas predicted condition, while parieto-occipital areas predicted direction in both conditions.
In a second EEG study (10 healthy subjects), we investigated how the results of the PTT translate to an online control scenario. In both conditions (hand movement based control, EEG based control) the kinematics could be decoded above the significance level, with parieto-occipital areas carrying significant information about end-effector velocity. We observed an amplitude-scaling mismatch between the decoded and actual kinematics, which resulted in declined tracking quality in the EEG based control condition.
To address the amplitude-scaling mismatch, we conducted an MEG study (20 healthy subjects), where we investigated a similar PTT as in the first EEG study. In this study, the subjects controlled the cursor with their right index finger [2]. We found that the MEG signals carried not only significant information about velocity, but also about speed in primary-sensorimotor areas.
We believe that our findings can aid to a new generation of non-invasive brain-computer interfaces.
[1] Kobler RJ et al. Sci.Rep. 2018 [2] Kobler RJ et al. Proc. 8thGBCIC. 2019(accepted)
Originalspracheenglisch
PublikationsstatusVeröffentlicht - 19 Okt 2019
VeranstaltungNeuroscience 2019 - Chicago, USA / Vereinigte Staaten
Dauer: 19 Okt 201923 Okt 2019
Konferenznummer: 49
https://www.sfn.org/meetings/neuroscience-2019

Konferenz

KonferenzNeuroscience 2019
LandUSA / Vereinigte Staaten
OrtChicago
Zeitraum19/10/1923/10/19
Internetadresse

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Biomechanical Phenomena
Healthy Volunteers
Kinematics
Upper Extremity
Hand
Brain-Computer Interfaces
Motor Cortex
Brain computer interface
Fingers
End effectors
Classifiers
Sensorimotor Cortex
Direction compound

Schlagwörter

    ASJC Scopus subject areas

    • !!Biomedical Engineering
    • !!Neuroscience (miscellaneous)
    • !!Signal Processing

    Fields of Expertise

    • Human- & Biotechnology

    Dies zitieren

    Kobler, R., Sburlea, A. I., Mondini, V., Kolesnichenko , E., Hashimoto, H., Hirata, M., & Müller-Putz, G. (2019). Expression of kinematic information in eeg and meg signals during visuomotor tasks. Abstract von Neuroscience 2019, Chicago, USA / Vereinigte Staaten.

    Expression of kinematic information in eeg and meg signals during visuomotor tasks. / Kobler, Reinmar; Sburlea, Andreea Ioana; Mondini, Valeria; Kolesnichenko , Elizaveta ; Hashimoto, Hiroaki; Hirata, Masayuki; Müller-Putz, Gernot.

    2019. Abstract von Neuroscience 2019, Chicago, USA / Vereinigte Staaten.

    Publikation: KonferenzbeitragAbstractForschungBegutachtung

    Kobler, R, Sburlea, AI, Mondini, V, Kolesnichenko , E, Hashimoto, H, Hirata, M & Müller-Putz, G 2019, 'Expression of kinematic information in eeg and meg signals during visuomotor tasks', Chicago, USA / Vereinigte Staaten, 19/10/19 - 23/10/19, .
    Kobler R, Sburlea AI, Mondini V, Kolesnichenko E, Hashimoto H, Hirata M et al. Expression of kinematic information in eeg and meg signals during visuomotor tasks. 2019. Abstract von Neuroscience 2019, Chicago, USA / Vereinigte Staaten.
    Kobler, Reinmar ; Sburlea, Andreea Ioana ; Mondini, Valeria ; Kolesnichenko , Elizaveta ; Hashimoto, Hiroaki ; Hirata, Masayuki ; Müller-Putz, Gernot. / Expression of kinematic information in eeg and meg signals during visuomotor tasks. Abstract von Neuroscience 2019, Chicago, USA / Vereinigte Staaten.
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    T1 - Expression of kinematic information in eeg and meg signals during visuomotor tasks

    AU - Kobler, Reinmar

    AU - Sburlea, Andreea Ioana

    AU - Mondini, Valeria

    AU - Kolesnichenko , Elizaveta

    AU - Hashimoto, Hiroaki

    AU - Hirata, Masayuki

    AU - Müller-Putz, Gernot

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    N2 - Electroencephalographic (EEG) and magnetoencephalographic (MEG) signals carry information about upper-limb kinematics. Their expression in M/EEG signals is potentially influenced by many factors. We believe that vision and movement type (i.e., discrete vs continuous) are key factors. To identify their effects, we investigated visuomotor tasks in several studies.In a first EEG study (15 healthy subjects), we investigated the influence of vision during goal-directed upper-limb movements [1]. Our paradigm implemented a center-out task (COT) followed by a pursuit tracking task (PTT). It involved two visual stimuli (target and cursor) in two conditions. In one condition (exe), subjects controlled the cursor with their right hand. In the second condition (obs), subjects observed a computer controlled cursor. Regarding the PTT, our findings indicate that premotor and primary sensorimotor areas carried significant information about cursor velocity in the exe condition, whereas parieto-occipital areas carried significant directional information in either condition. Regarding the COT, we could classify condition (exe vs obs) and direction (left, up, right and down) above significance level. The two classifiers relied on different cortical areas. Contra-lateral pre- and primary motor areas predicted condition, while parieto-occipital areas predicted direction in both conditions.In a second EEG study (10 healthy subjects), we investigated how the results of the PTT translate to an online control scenario. In both conditions (hand movement based control, EEG based control) the kinematics could be decoded above the significance level, with parieto-occipital areas carrying significant information about end-effector velocity. We observed an amplitude-scaling mismatch between the decoded and actual kinematics, which resulted in declined tracking quality in the EEG based control condition.To address the amplitude-scaling mismatch, we conducted an MEG study (20 healthy subjects), where we investigated a similar PTT as in the first EEG study. In this study, the subjects controlled the cursor with their right index finger [2]. We found that the MEG signals carried not only significant information about velocity, but also about speed in primary-sensorimotor areas.We believe that our findings can aid to a new generation of non-invasive brain-computer interfaces.[1] Kobler RJ et al. Sci.Rep. 2018 [2] Kobler RJ et al. Proc. 8thGBCIC. 2019(accepted)

    AB - Electroencephalographic (EEG) and magnetoencephalographic (MEG) signals carry information about upper-limb kinematics. Their expression in M/EEG signals is potentially influenced by many factors. We believe that vision and movement type (i.e., discrete vs continuous) are key factors. To identify their effects, we investigated visuomotor tasks in several studies.In a first EEG study (15 healthy subjects), we investigated the influence of vision during goal-directed upper-limb movements [1]. Our paradigm implemented a center-out task (COT) followed by a pursuit tracking task (PTT). It involved two visual stimuli (target and cursor) in two conditions. In one condition (exe), subjects controlled the cursor with their right hand. In the second condition (obs), subjects observed a computer controlled cursor. Regarding the PTT, our findings indicate that premotor and primary sensorimotor areas carried significant information about cursor velocity in the exe condition, whereas parieto-occipital areas carried significant directional information in either condition. Regarding the COT, we could classify condition (exe vs obs) and direction (left, up, right and down) above significance level. The two classifiers relied on different cortical areas. Contra-lateral pre- and primary motor areas predicted condition, while parieto-occipital areas predicted direction in both conditions.In a second EEG study (10 healthy subjects), we investigated how the results of the PTT translate to an online control scenario. In both conditions (hand movement based control, EEG based control) the kinematics could be decoded above the significance level, with parieto-occipital areas carrying significant information about end-effector velocity. We observed an amplitude-scaling mismatch between the decoded and actual kinematics, which resulted in declined tracking quality in the EEG based control condition.To address the amplitude-scaling mismatch, we conducted an MEG study (20 healthy subjects), where we investigated a similar PTT as in the first EEG study. In this study, the subjects controlled the cursor with their right index finger [2]. We found that the MEG signals carried not only significant information about velocity, but also about speed in primary-sensorimotor areas.We believe that our findings can aid to a new generation of non-invasive brain-computer interfaces.[1] Kobler RJ et al. Sci.Rep. 2018 [2] Kobler RJ et al. Proc. 8thGBCIC. 2019(accepted)

    KW - brain-computer interface

    KW - Electroencephalogram (EEG)

    KW - magnetoencephalography

    KW - kinematics

    KW - visuomotor

    KW - oculomotor

    M3 - Abstract

    ER -