Decoding Movements of the Upper Limb from EEG

Patrick Ofner; Andreas Schwarz; Joana Pereira; Gernot Müller-Putz

Decoding Movements of the Upper Limb from EEG

Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot Müller-Putz

Institute of Neural Engineering (7090)

Research output: Contribution to conference › Poster

Abstract

A neuroprosthesis can restore movement functions of persons with spinal cord injury. It benefits from a brain-computer interface (BCI) with a high number of control classes. However, classical sensorimotor rhythm-based BCIs can often only provide less than 3 classes, and new types of BCIs need to be developed. We investigated whether low-frequency time-domain signals (i.e. movement-related cortical potentials) can be used to classify hand/arm movements of the same limb. A BCI based on attempted movements may be used to control a neuroprosthesis more naturally and provide a higher number of control classes.

Original language	English
Publication status	Published - 20 Jun 2017
Event	cuttingEEG - Glasgow, United Kingdom Duration: 19 Jun 2017 → 22 Jun 2017

Workshop

Workshop	cuttingEEG
Country/Territory	United Kingdom
City	Glasgow
Period	19/06/17 → 22/06/17

Fields of Expertise

Human- & Biotechnology

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)

Access to Document

Poster_Ofner

EU - Feel Your Reach - Non-invasive decoding of cortical patterns induced by goal directed movement intentions and artificial sensory feedback in humans
Müller-Putz, G.
1/05/16 → 31/07/21
Project: Research project
MoreGrasp - Restoration of upper limb function in individuals with high spinal cord injury by multimodal neuroprostheses for interaction in daily activities
Müller-Putz, G.
1/03/15 → 31/05/18
Project: Research project

Cite this

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title = "Decoding Movements of the Upper Limb from EEG",

abstract = "A neuroprosthesis can restore movement functions of persons with spinal cord injury. It benefits from a brain-computer interface (BCI) with a high number of control classes. However, classical sensorimotor rhythm-based BCIs can often only provide less than 3 classes, and new types of BCIs need to be developed. We investigated whether low-frequency time-domain signals (i.e. movement-related cortical potentials) can be used to classify hand/arm movements of the same limb. A BCI based on attempted movements may be used to control a neuroprosthesis more naturally and provide a higher number of control classes.",

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T1 - Decoding Movements of the Upper Limb from EEG

AU - Ofner, Patrick

AU - Schwarz, Andreas

AU - Pereira, Joana

AU - Müller-Putz, Gernot

PY - 2017/6/20

Y1 - 2017/6/20

N2 - A neuroprosthesis can restore movement functions of persons with spinal cord injury. It benefits from a brain-computer interface (BCI) with a high number of control classes. However, classical sensorimotor rhythm-based BCIs can often only provide less than 3 classes, and new types of BCIs need to be developed. We investigated whether low-frequency time-domain signals (i.e. movement-related cortical potentials) can be used to classify hand/arm movements of the same limb. A BCI based on attempted movements may be used to control a neuroprosthesis more naturally and provide a higher number of control classes.

AB - A neuroprosthesis can restore movement functions of persons with spinal cord injury. It benefits from a brain-computer interface (BCI) with a high number of control classes. However, classical sensorimotor rhythm-based BCIs can often only provide less than 3 classes, and new types of BCIs need to be developed. We investigated whether low-frequency time-domain signals (i.e. movement-related cortical potentials) can be used to classify hand/arm movements of the same limb. A BCI based on attempted movements may be used to control a neuroprosthesis more naturally and provide a higher number of control classes.

M3 - Poster

T2 - cuttingEEG

Y2 - 19 June 2017 through 22 June 2017

ER -

Decoding Movements of the Upper Limb from EEG

Abstract

Workshop

Fields of Expertise

Treatment code (Nähere Zuordnung)

Access to Document

Fingerprint

Projects

EU - Feel Your Reach - Non-invasive decoding of cortical patterns induced by goal directed movement intentions and artificial sensory feedback in humans

MoreGrasp - Restoration of upper limb function in individuals with high spinal cord injury by multimodal neuroprostheses for interaction in daily activities

Cite this