Evidence map›Paper›PMID 40857498›Full record

ArticleJournal of the American Statistical Association2025

Bayesian Inference on Brain-Computer Interfaces via GLASS.

Bangyao Zhao, Jane E Huggins, Jian Kang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2025. 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

3 authors.

Bangyao ZhaoDepartment of Biostatistics, University of Michigan.
Jane E HugginsDepartment of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan.
Jian KangDepartment of Biostatistics, University of Michigan.

Funding

Statistical ICA Methods for Analysis & Integration of Multi-dimensional Data Diversity SupplementR01MH105561 · NIMH · EMORY UNIVERSITY · PI GUO, YING, KANG, JIAN · 2014 to 2024
$4.3M
Scalable Bayesian methods for big imaging data analysisR01DA048993 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI JOHNSON, TIMOTHY D, KANG, JIAN · 2020 to 2024
$1.6M
Bayesian Network Biomarker Selection in Metabolomics DataR01GM124061 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KANG, JIAN · 2017 to 2020
$1.4M
Brain Computer Interfaces for the Operation of Commercial Assistive TechnologyR21HD054697 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HUGGINS, JANE E · 2008 to 2009
$391k
NICHD NIH HHS R21 HD054697NIDA NIH HHS R01 DA048993NIGMS NIH HHS R01 GM124061NIMH NIH HHS R01 MH105561
6 · The paper itself

Abstract

Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary users. To address these challenges, we introduce a novel

Indexed as

Bayesian AnalysisERPGaussian ProcessLatent ChannelP300

Identifiers

PMID40857498
PMCPMC12369995

What OpenQuestion holds

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Registered trials

None linked

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.