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Cited by 477EEG and Brain-Computer InterfacesStroke Rehabilitation and RecoveryMuscle activation and electromyography studies

EEG-based neuroprosthesis control: A step towards clinical practice

Müller-Putz, Gernot R., Scherer, Reinhold, Pfurtscheller, Gert, Rupp, Rüdiger·2005·Neuroscience Letters
DOI 10.1016/j.neulet.2005.03.021
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Abstract

This case study demonstrates the coupling of an electroencephalogram (EEG)-based Brain-Computer Interface (BCI) with an implanted neuroprosthesis (Freehand system). Because the patient was available for only 3 days, the goal was to demonstrate the possibility of a patient gaining control over the motor imagery-based Graz BCI system within a very short training period. By applying himself to an organized and coordinated training procedure, the patient was able to generate distinctive EEG-patterns by the imagination of movements of his paralyzed left hand. These patterns consisted of power decreases in specific frequency bands that could be classified by the BCI. The output signal of the BCI emulated the shoulder joystick usually used, and by consecutive imaginations the patient was able to switch between different grasp phases of the lateral grasp that the Freehand system provided. By performing a part of the grasp-release test, the patient was able to move a simple object from one place to another. The results presented in this work give evidence that Brain-Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions. The fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes.

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Crossref · 10.1016/j.neulet.2005.03.021

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  • Mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy or application performances.

    “Indeed, mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy [25,27,30–35] or application performances [36,37].”

    From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [31]

  • Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of users to voluntarily modulate their brain signals (subject learning) is scarce, most often indirect and rather inconclusive.

    “Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of the users to voluntary modulate their brain signals (subject learning) is, in fact, scarce, most often indirect and rather inconclusive.”

    From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [31]

  • Other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy or application performances.

    “Secondly, other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy [9,27,30,31,33,35,51] or application performances [36,37].”

    From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [31]

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Authors
Müller-Putz, G. R., Scherer, R., Pfurtscheller, G., & Rupp, R.
Affiliations
  • Graz University of Technology (AT)
  • Heidelberg University (DE)
ORCID
Venue
Neuroscience Letters
Publisher
Elsevier BV
Published
2005-07
Volume
382
Issue
1-2
Pages
169-174
ISSN
0304-3940 (ISSN-L)
DOI
10.1016/j.neulet.2005.03.021
OpenAlex
W2088809939
Cited by
477
References
20
Citations by year
2026: 6 · 2025: 5 · 2024: 12 · 2023: 13 · 2022: 11 · 2021: 26 · 2020: 24 · 2019: 31 · 2018: 28 · 2017: 21 · 2016: 23 · 2015: 30 · 2014: 40 · 2013: 40 · 2012: 30
FWCI
10.71
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Neuroscience Letters (publishedVersion)
  • PubMed (publishedVersion)
Funders
Allgemeine Unfallversicherungsanstalt
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Keywords
brain-computer interface, motor imagery, high spinal cord injury, EEG classification
MeSH terms
Adult, Brain, Electric Stimulation, Electrodes, Implanted, Electroencephalography, Foot, Hand, Humans, Imagination, Male, Motor Skills, Movement, Quadriplegia, Refractory Period, Electrophysiological, Spinal Cord Injuries, Hand Strength, Prostheses and Implants

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