Fast Set-up Asynchronous Brain-switch Based on Detection of Foot Motor Imagery in 1-channel EEG

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2010 Jan 7

PMID 20052556

Citations 27

Authors

Gernot R Muller-Putz

Vera Kaiser

Teodoro Solis-Escalante

Gert Pfurtscheller

Affiliations

Soon will be listed here.

Abstract

Bringing a Brain-Computer Interface (BCI) out of the lab one of the main problems has to be solved: to shorten the training time. Finding a solution for this problem, the use of a BCI will be open not only for people who have no choice, e.g., persons in a locked-in state, or suffering from a degenerating nerve disease. By reducing the training time to a minimum, also healthy persons will make use of the system, e.g., for using this kind of control for games. For realizing such a control, the post-movement beta rebound occurring after brisk feet movement was used to set up a classifier. This classifier was then used in a cue-based motor imagery system. After classifier adaptation, a self-paced brain-switch based on brisk foot motor imagery (MI) was evaluated. Four out of six subjects showed that a post-movement beta rebound after feet MI and succeeded with a true positive rate between 69 and 89%, while the positive predictive value was between 75 and 93%.

Citing Articles

A novel paradigm for fast training data generation in asynchronous movement-based BCIs.

Crell M, Kostoglou K, Sterk K, Muller-Putz G Front Hum Neurosci. 2025; 19:1540155.

PMID: 40007882 PMC: 11850329. DOI: 10.3389/fnhum.2025.1540155.

Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.

Edelman B, Zhang S, Schalk G, Brunner P, Muller-Putz G, Guan C IEEE Rev Biomed Eng. 2024; 18:26-49.

PMID: 39186407 PMC: 11861396. DOI: 10.1109/RBME.2024.3449790.

Neural correlates of motor imagery and execution in real-world dynamic behavior: evidence for similarities and differences.

Mustile M, Kourtis D, Edwards M, Donaldson D, Ietswaart M Front Hum Neurosci. 2024; 18:1412307.

PMID: 38974480 PMC: 11224467. DOI: 10.3389/fnhum.2024.1412307.

An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study.

Song M, Jeong H, Kim J, Jang S, Kim J Front Neurorobot. 2022; 16:971547.

PMID: 36172602 PMC: 9510756. DOI: 10.3389/fnbot.2022.971547.

Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.

Korik A, McCreadie K, McShane N, Bois N, Khodadadzadeh M, Stow J J Neuroeng Rehabil. 2022; 19(1):95.

PMID: 36068570 PMC: 9446658. DOI: 10.1186/s12984-022-01073-9.

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