Evidence map›Paper›PMID 40563723›Full record

ArticleBrain sciences2025

Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.

Milán András Fodor, Atilla Cantürk, Gernot Heisenberg, Ivan Volosyak

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In one paragraph

Article in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Milán András FodorFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany.ORCID 0009-0009-9769-0821
Atilla CantürkFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany.ORCID 0000-0003-4624-1283
Gernot HeisenbergInstitute of Information Science, Technical University of Applied Sciences Cologne, 50678 Cologne, Germany.ORCID 0000-0002-1786-8485
Ivan VolosyakFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany.ORCID 0000-0001-6555-7617

Funding

This project has received funding from the European Union's research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101118964. 101118964
6 · The paper itself

Abstract

(1) Background: Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals, offering potential applications in assistive technology and neurorehabilitation. Code-modulated visual evoked potential (cVEP)-based BCIs employ code-pattern-based stimulation to evoke neural responses, which can then be classified to infer user intent. While increasing the number of EEG electrodes across the visual cortex enhances classification accuracy, it simultaneously reduces user comfort and increases setup complexity, duration, and hardware costs. (2) Methods: This online BCI study, involving thirty-eight able-bodied participants, investigated how reducing the electrode count from 16 to 6 affected performance. Three experimental conditions were tested: a baseline 16-electrode configuration, a reduced 6-electrode setup without retraining, and a reduced 6-electrode setup with retraining. (3) Results: Our results indicate that, on average, performance declines with fewer electrodes; nonetheless, retraining restored near-baseline mean Information Transfer Rate (ITR) and accuracy for those participants for whom the system remained functional. The results reveal that for a substantial number of participants, the classification pipeline fails after electrode removal, highlighting individual differences in the cVEP response characteristics or inherent limitations of the classification approach. (4) Conclusions: Ultimately, this suggests that minimal cVEP-BCI electrode setups capable of reliably functioning across all users might only be feasible through other, more flexible classification methods that can account for individual differences. These findings aim to serve as a guideline for what is currently achievable with this common cVEP paradigm and to highlight where future research should focus in order to move closer to a practical and user-friendly system.

Indexed as

BCI spellerbrain–computer interface (BCI)code-modulated visual evoked potential (cVEP)EEG-based BCIelectrode reductionvisual evoked potential (VEP)

Identifiers

PMID40563723
PMCPMC12190882

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.