Evidence map›Paper›PMID 38328139›Full record

ArticlebioRxiv : the preprint server for biology2024

fNIRS Dataset During Complex Scene Analysis.

Matthew Ning, Sudan Duwadi, Meryem A Yücel, Alexander Von Lühmann, David A Boas, Kamal Sen

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Matthew NingBerenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Sudan DuwadiNeurophotonics Center, Department of Biomedical Engineering, Boston University.
Meryem A YücelNeurophotonics Center, Department of Biomedical Engineering, Boston University.
Alexander Von LühmannNeurophotonics Center, Department of Biomedical Engineering, Boston University.
David A BoasNeurophotonics Center, Department of Biomedical Engineering, Boston University.
Kamal SenNeurophotonics Center, Department of Biomedical Engineering, Boston University.
Boston University · USBeth Israel Deaconess Medical Center · US

Funding

Advanced Research Training in Communication Sciences and DisordersT32DC013017 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Cara E. Stepp · 2016 to 2026
$4.9M
NIDCD NIH HHS T32 DC013017
6 · The paper itself

Abstract

When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location. The ability to decode the attended spatial location would facilitate brain computer interfaces for complex scene analysis (CSA). Here, we investigated capability of functional near-infrared spectroscopy (fNIRS) to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. We targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. To date, fNIRS has not been applied to decode auditory and visual-spatial attention during CSA, and thus, no such dataset exists yet. This report provides an open-access fNIRS dataset that can be used to develop, test, and compare machine learning algorithms for classifying attended locations based on the fNIRS signals on a single trial basis.

Indexed as

BCIcomplex scene analysisfNIRSopen accessspatial attention

Identifiers

PMID38328139
PMCPMC10849700
OpenAlexW4391131437

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.