Predicting high-quality movements in post-stroke motor rehabilitation from EEG

Philipp Raggam; Christoph Zrenner; Eric J. McDermott; Ulf Ziemann; Moritz Grosse-Wentrup

Predicting high-quality movements in post-stroke motor rehabilitation from EEG

Philipp Raggam, Christoph Zrenner, Eric J. McDermott, Ulf Ziemann, Moritz Grosse-Wentrup

Publications: Contribution to conference › Other contribution to conference › Peer Reviewed

Abstract

A promising new concept for post-stroke motor rehabilitation is using EEG-based brain-computer interface (BCI) systems, e.g., providing patients with EEG-based feedback on their decoded movement intent. Here, we explore the possibility of extending BCI-based rehabilitation paradigms from decoding movement intent to decoding movement quality. Toward this goal, we study whether the quality of hand opening and closing movements in stroke patients with arm and hand spasticity can be decoded from their EEG.

Original language	English
Publication status	Published - 6 Jun 2023
Event	10th International BCI Meeting - Sonian Forest, Brussels, Belgium Duration: 6 Jun 2023 → 9 Jun 2023 https://bcisociety.org/bci-meeting/

Conference

Conference	10th International BCI Meeting
Country/Territory	Belgium
City	Brussels
Period	6/06/23 → 9/06/23
Internet address	https://bcisociety.org/bci-meeting/

Austrian Fields of Science 2012

301401 Brain research
102001 Artificial intelligence

Keywords

Stroke Rehabilitation
EEG
Movement quality prediction
Machine Learning
Neurophysiology

Cite this

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title = "Predicting high-quality movements in post-stroke motor rehabilitation from EEG",

abstract = "A promising new concept for post-stroke motor rehabilitation is using EEG-based brain-computer interface (BCI) systems, e.g., providing patients with EEG-based feedback on their decoded movement intent. Here, we explore the possibility of extending BCI-based rehabilitation paradigms from decoding movement intent to decoding movement quality. Toward this goal, we study whether the quality of hand opening and closing movements in stroke patients with arm and hand spasticity can be decoded from their EEG.",

keywords = "Stroke Rehabilitation, EEG, Movement quality prediction, Machine Learning, Neurophysiology",

author = "Philipp Raggam and Christoph Zrenner and McDermott, {Eric J.} and Ulf Ziemann and Moritz Grosse-Wentrup",

year = "2023",

month = jun,

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note = "10th International BCI Meeting ; Conference date: 06-06-2023 Through 09-06-2023",

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TY - CONF

T1 - Predicting high-quality movements in post-stroke motor rehabilitation from EEG

AU - Raggam, Philipp

AU - Zrenner, Christoph

AU - McDermott, Eric J.

AU - Ziemann, Ulf

AU - Grosse-Wentrup, Moritz

PY - 2023/6/6

Y1 - 2023/6/6

N2 - A promising new concept for post-stroke motor rehabilitation is using EEG-based brain-computer interface (BCI) systems, e.g., providing patients with EEG-based feedback on their decoded movement intent. Here, we explore the possibility of extending BCI-based rehabilitation paradigms from decoding movement intent to decoding movement quality. Toward this goal, we study whether the quality of hand opening and closing movements in stroke patients with arm and hand spasticity can be decoded from their EEG.

AB - A promising new concept for post-stroke motor rehabilitation is using EEG-based brain-computer interface (BCI) systems, e.g., providing patients with EEG-based feedback on their decoded movement intent. Here, we explore the possibility of extending BCI-based rehabilitation paradigms from decoding movement intent to decoding movement quality. Toward this goal, we study whether the quality of hand opening and closing movements in stroke patients with arm and hand spasticity can be decoded from their EEG.

KW - Stroke Rehabilitation

KW - EEG

KW - Movement quality prediction

KW - Machine Learning

KW - Neurophysiology

UR - https://bcisociety.org/wp-content/uploads/2023/06/Proceedings2023_V9.pdf

M3 - Other contribution to conference

T2 - 10th International BCI Meeting

Y2 - 6 June 2023 through 9 June 2023

ER -

Predicting high-quality movements in post-stroke motor rehabilitation from EEG

Abstract

Conference

Austrian Fields of Science 2012

Keywords

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