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

doi:10.3217/978-3-85125-826-4-22

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

Institute of Neural Engineering (7090)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-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 language	English
Title of host publication	Proceedings Annual Meeting of the Austrian Society for Biomedical Engineering 2021
Subtitle of host publication	ÖGBMT 2021
Publisher	Verlag der Technischen Universität Graz
Pages	83-86
Number of pages	4
DOIs	https://doi.org/10.3217/978-3-85125-826-4-22
Publication status	Published - 2021
Event	Annual Meeting of the Austrian Society of the Biomedical Engineering 2021: ÖGBMT 2021 - Graz University of Technology, Graz, Austria Duration: 30 Sept 2021 → 1 Oct 2021 https://oegbmt2021.tugraz.at/

Conference

Conference	Annual Meeting of the Austrian Society of the Biomedical Engineering 2021
Abbreviated title	ÖGBMT 2021
Country/Territory	Austria
City	Graz
Period	30/09/21 → 1/10/21
Internet address	https://oegbmt2021.tugraz.at/

Keywords

Electroencephalography (EEG)
trajectory decoding
learning effects

Access to Document

10.3217/978-3-85125-826-4-22Licence: CC BY 4.0

file:///C:/Users/pulferer/AppData/Local/Temp/Proceedings_OEGBMT2021.pdf

Cite this

Pulferer, H., Ásgeirsdóttir, B., Mondini, V., Sburlea, A. I., & Müller-Putz, G. (2021). Learning effects in 2D trajectory inference from low-frequency EEG signals over multiple feedback sessions. In Proceedings Annual Meeting of the Austrian Society for Biomedical Engineering 2021: ÖGBMT 2021 (pp. 83-86). Verlag der Technischen Universität Graz. https://doi.org/10.3217/978-3-85125-826-4-22

Learning effects in 2D trajectory inference from low-frequency EEG signals over multiple feedback sessions. / Pulferer, Hannah; Ásgeirsdóttir, Brynja; Mondini, Valeria et al.
Proceedings Annual Meeting of the Austrian Society for Biomedical Engineering 2021: ÖGBMT 2021. Verlag der Technischen Universität Graz, 2021. p. 83-86.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Pulferer, H, Ásgeirsdóttir, B, Mondini, V, Sburlea, AI & Müller-Putz, G 2021, Learning effects in 2D trajectory inference from low-frequency EEG signals over multiple feedback sessions. in Proceedings Annual Meeting of the Austrian Society for Biomedical Engineering 2021: ÖGBMT 2021. Verlag der Technischen Universität Graz, pp. 83-86, Annual Meeting of the Austrian Society of the Biomedical Engineering 2021, Graz, Austria, 30/09/21. https://doi.org/10.3217/978-3-85125-826-4-22

@inproceedings{b5e38d69345546e4b6e476e8649ef026,

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

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.",

keywords = "Electroencephalography (EEG), trajectory decoding, learning effects",

author = "Hannah Pulferer and Brynja {\'A}sgeirsd{\'o}ttir and Valeria Mondini and Sburlea, {Andreea Ioana} and Gernot M{\"u}ller-Putz",

year = "2021",

doi = "10.3217/978-3-85125-826-4-22",

language = "English",

pages = "83--86",

booktitle = "Proceedings Annual Meeting of the Austrian Society for Biomedical Engineering 2021",

publisher = "Verlag der Technischen Universit{\"a}t Graz",

note = "Annual Meeting of the Austrian Society of the Biomedical Engineering 2021 : {\"O}GBMT 2021, {\"O}GBMT 2021 ; Conference date: 30-09-2021 Through 01-10-2021",

url = "https://oegbmt2021.tugraz.at/",

}

TY - GEN

T1 - Learning effects in 2D trajectory inference from low-frequency EEG signals over multiple feedback sessions

AU - Pulferer, Hannah

AU - Ásgeirsdóttir, Brynja

AU - Mondini, Valeria

AU - Sburlea, Andreea Ioana

AU - Müller-Putz, Gernot

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Electroencephalography (EEG)

KW - trajectory decoding

KW - learning effects

U2 - 10.3217/978-3-85125-826-4-22

DO - 10.3217/978-3-85125-826-4-22

M3 - Conference paper

SP - 83

EP - 86

BT - Proceedings Annual Meeting of the Austrian Society for Biomedical Engineering 2021

PB - Verlag der Technischen Universität Graz

T2 - Annual Meeting of the Austrian Society of the Biomedical Engineering 2021

Y2 - 30 September 2021 through 1 October 2021

ER -

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

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this