Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury

Patrick Ofner, Andreas Schwarz, Joana Pereira, Daniela Wyss, Renate Wildburger, Gernot R. Müller-Putz*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).

Original languageEnglish
Article number7134
Pages (from-to)7134
JournalScientific Reports
Volume9
Issue number1
DOIs
Publication statusPublished - 9 May 2019

ASJC Scopus subject areas

  • Biomedical Engineering
  • Neuroscience (miscellaneous)

Fields of Expertise

  • Human- & Biotechnology

Fingerprint Dive into the research topics of 'Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury'. Together they form a unique fingerprint.

  • Projects

    Cite this