CoChat
Sign Up
Cited by 526EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyMuscle activation and electromyography studies

How many people are able to operate an EEG-based brain-computer interface (BCI)?

Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., Pfurtscheller, G.·2003·IEEE Transactions on Neural Systems and Rehabilitation Engineering
DOI 10.1109/tnsre.2003.814481
Open this paper in CoChat — free account

Abstract

Ninety-nine healthy people participated in a brain-computer interface (BCI) field study conducted at an exposition held in Graz, Austria. Each subject spent 20-30 min on a two-session BCI investigation. The first session consisted of 40 trials conducted without feedback. Then, a subject-specific classifier was set up to provide the subject with feedback, and the second session--40 trials in which the subject had to control a horizontal bar on a computer screen--was conducted. Subjects were instructed to imagine a right-hand movement or a foot movement after a cue stimulus depending on the direction of an arrow. Bipolar electrodes were mounted over the right-hand representation area and over the foot representation area. Classification results achieved with 1) an adaptive autoregressive model (39 subjects) and 2) band power estimation (60 subjects) are presented. Roughly 93% of the subjects were able to achieve classification accuracy above 60% after two sessions of training.

Integrity signals

Registry

Crossref · 10.1109/tnsre.2003.814481

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • General concerns about non-universal usability, robustness, and the role of training raised by able-bodied user studies are even more pressing with regard to end-user populations.

    “As a result, the general concerns about the non-universal usability, robustness, and, especially, the role of training raised by able-bodied user studies [3–7] are even more pressing with regard to end-user populations.”

    From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [3]

  • Works where users acquired BCI control, in able-bodied and end-user populations, do not report any learning metric over time.

    “Firstly, works where users acquired BCI control (able-bodied [3,25] and end-users [40,41,51,62,63]) do not report any learning metric over time.”

    From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [3]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., & Pfurtscheller, G.
Affiliations
  • Guger Technologies (Austria) (AT)
  • Graz University of Technology (AT)
Venue
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2003-06
Volume
11
Issue
2
Pages
145-147
ISSN
  • 1534-4320 (ISSN-L)
  • 1558-0210
DOI
10.1109/tnsre.2003.814481
OpenAlex
W2149690083
Cited by
526
References
16
Citations by year
2026: 13 · 2025: 16 · 2024: 22 · 2023: 15 · 2022: 20 · 2021: 30 · 2020: 26 · 2019: 28 · 2018: 33 · 2017: 35 · 2016: 29 · 2015: 45 · 2014: 37 · 2013: 26 · 2012: 33
FWCI
2.12
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Keywords
EEG-based brain-computer interface, motor imagery, adaptive autoregressive model, BCI usability, fieldwork
MeSH terms
Adaptation, Physiological, Adult, Brain, Electroencephalography, Evoked Potentials, Evoked Potentials, Visual, Feedback, Humans, Photic Stimulation, Psychomotor Performance, Sensitivity and Specificity, Task Performance and Analysis, Thinking, User-Computer Interface, Visual Perception, Reproducibility of Results

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review