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Research data . Dataset . 2017

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

Ofner, Patrick; Schwarz, Andreas; Pereira, Joana; Müller-Putz, Gernot R.;
Open Access
Published: 10 Aug 2017
Publisher: Zenodo
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.
Please cite the paper http://dx.doi.org/10.1371/journal.pone.0182578 if you use this dataset.
Subjects

brain-computer interface

Funded by
EC| Feel your Reach
Project
Feel your Reach
Non-invasive decoding of cortical patterns induced by goal directed movement intentions and artificial sensory feedback in humans
  • Funder: European Commission (EC)
  • Project Code: 681231
  • Funding stream: H2020 | ERC | ERC-COG
,
EC| MoreGrasp
Project
MoreGrasp
Restoration of upper limb function in individuals with high spinal cord injury by multimodal neuroprostheses for interaction in daily activities
  • Funder: European Commission (EC)
  • Project Code: 643955
  • Funding stream: H2020 | RIA
Validated by funder
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Rural Digital Europe
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