Learning effects in 2D trajectory inference from low-frequency EEG signals over multiple feedback sessions

Hannah Pulferer, Brynja Ásgeirsdóttir, Valeria Mondini, Andreea Ioana Sburlea, Gernot Müller-Putz

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Recent research from our group has shown that non-invasive continuous online decoding of executed movement from non-invasive low-frequency brain signals is feasible. In order to cater the setup to actual end users, we proposed a new paradigm based on attempted movement and after con-
ducting a pilot study, we hypothesize that user control in this setup may be improved by learning over multiple sessions. Over three sessions within five days, we acquired 60-channel electroencephalographic (EEG) signals from nine able-bodied participants while having them track a moving target / trace depicted shapes on a screen. Though no global learning effect could be
identified, increases in correlations between target and decoded trajectories for approximately half of the participants could be observed.
Original languageEnglish
Title of host publicationProceedings Annual Meeting of the Austrian Society for Biomedical Engineering 2021
Subtitle of host publicationÖGBMT 2021
PublisherVerlag der Technischen Universität Graz
Pages83-86
Number of pages4
DOIs
Publication statusPublished - 2021
EventAnnual Meeting of the Austrian Society of the Biomedical Engineering 2021: ÖGBMT 2021 - Graz University of Technology, Graz, Austria
Duration: 30 Sep 20211 Oct 2021
https://oegbmt2021.tugraz.at/

Conference

ConferenceAnnual Meeting of the Austrian Society of the Biomedical Engineering 2021
Abbreviated titleÖGBMT 2021
Country/TerritoryAustria
CityGraz
Period30/09/211/10/21
Internet address

Keywords

  • Electroencephalography (EEG)
  • trajectory decoding
  • learning effects

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