Evidence map›Paper›PMID 41735460›Full record

ArticleCommunications engineering2026

Increasing EEG electrode density improves decoding of visual categories and source localization: an exploratory ultra-high-density EEG study.

Leonhard Schreiner, Sebastian Sieghartsleitner, Christoph Kapeller, Michael Jordan, Josef Scharinger, Rossella Spataro, Harald Pretl, Christoph Guger

Abstract read
In one paragraph

Article in Communications engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Leonhard Schreiner *g.tec medical engineering GmbH, Schiedlberg, Austria. leonhard.schreiner@stanford.edu.ORCID http://orcid.org/0009-0004-3272-6866
Sebastian Sieghartsleitner *g.tec medical engineering GmbH, Schiedlberg, Austria.ORCID http://orcid.org/0009-0007-9222-9409
Christoph Kapellerg.tec medical engineering GmbH, Schiedlberg, Austria.ORCID http://orcid.org/0000-0002-7330-895X
Michael Jordang.tec medical engineering GmbH, Schiedlberg, Austria.
Josef ScharingerInstitute of Computational Perception, Johannes Kepler University, Linz, Austria.
Rossella SpataroIntensive Neurorehabilitation Unit, Villa delle Ginestre Hospital, Palermo, Italy.
Harald PretlInstitute for Integrated Circuits, Johannes Kepler University, Linz, Austria.
Christoph Gugerg.tec medical engineering GmbH, Schiedlberg, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding how the brain processes visual information is central to cognitive neuroscience. This exploratory study investigates the potential of ultra-high-density (uHD) electroencephalography (EEG) to capture neural dynamics during visual processing. We demonstrate that increasing EEG electrode density enhances spatial precision, improves the decoding of visual information, and facilitates more accurate source localization. Using a uHD EEG system with 512 electrodes focused over the occipital region, we recorded brain responses to four visual categories: faces, bodies, objects, and patterns. Our results show that higher electrode density facilitates more accurate single-trial classification of visual stimuli, achieving an average accuracy of 73% using uHD EEG and regularized linear discriminant analysis. Comparisons across different electrode layouts revealed that reducing electrode distance substantially affects decoding performance more than expanding the electrode coverage area. Additionally, higher electrode density resulted in more detailed topographic activation maps and improved source localization of the face-selective N170 component in the fusiform gyrus. While based on a limited number of participants, these findings highlight the critical role of electrode layout in visual EEG paradigms and demonstrate the promise of uHD EEG for high-resolution investigations across both research and clinical domains.

Identifiers

PMID41735460
PMCPMC13043922

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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.