Upper limb movements can be decoded from the time-domain of low-frequency EEG

Research output: Contribution to journalArticleResearchpeer-review

Abstract

How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.

Original languageEnglish
Pages (from-to)e0182578
JournalPLoS ONE
Volume12
Issue number8
DOIs
Publication statusPublished - 10 Aug 2017

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electroencephalography
Electroencephalography
limbs (animal)
Upper Extremity
Motor Cortex
Brain
cortex
Classifiers
people with disabilities
Disabled persons
brain
Robotic arms
Parietal Lobe
Somatosensory Cortex
Robotics
Conveying
Disabled Persons
Decoding
Healthy Volunteers
methodology

Fields of Expertise

  • Human- & Biotechnology

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)
  • Experimental

Cite this

Upper limb movements can be decoded from the time-domain of low-frequency EEG. / Ofner, Patrick; Schwarz, Andreas; Pereira, Joana; Müller-Putz, Gernot R.

In: PLoS ONE, Vol. 12, No. 8, 10.08.2017, p. e0182578.

Research output: Contribution to journalArticleResearchpeer-review

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