Hierarchical decoding of grasping commands from EEG

Jason Omedes*, Andreas Schwarz, Luis Montesano, Gernot Muller-Putz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Brain-Computer Interfaces may present an intuitive way for motor impaired end users to operate assistive devices of daily life. Recent studies showed that complex kinematics like grasping can be successfully decoded from low frequency electroencephalogram. In this work we present a hierarchical method to asynchronously discriminate two different grasps often used in daily life actions (palmar, pincer) from a combined set of motor execution and motor intention. We compared sensorimotor rhythms based features and time features from the low frequency spectrum for best discrimination results. Our results show not only the principle feasibility of the proposed method with detection of asynchronous motor intention at rates of 80% accuracy and subsequent grasping discrimination over 60%, but also that low frequency time domain features provide a more consistent detection pattern. Although the basis of this results is still an off-line analysis we are confident that these results can be transferred to on-line use.

Original languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages2085-2088
Number of pages4
ISBN (Electronic)9781509028092
DOIs
Publication statusPublished - 13 Sep 2017
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
CountryKorea, Republic of
CityJeju Island
Period11/07/1715/07/17

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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