Evidence map›Paper›PMID 36481698›Full record

ArticleCommunications biology2022

Tracking momentary fluctuations in human attention with a cognitive brain-machine interface.

Abhijit M Chinchani, Siddharth Paliwal, Suhas Ganesh, Vishnu Chandrasekhar, Byron M Yu, Devarajan Sridharan

Open access · goldAbstract read
In one paragraph

Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
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

2 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
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

6 authors at 3 institutions in 3 countries.

Abhijit M Chinchani *Centre for Neuroscience, Indian Institute of Science, Bangalore, KA, India.ORCID 0000-0001-5950-4332
Siddharth Paliwal *Centre for Neuroscience, Indian Institute of Science, Bangalore, KA, India.ORCID 0000-0002-1388-2095
Suhas GaneshCentre for Neuroscience, Indian Institute of Science, Bangalore, KA, India.
Vishnu ChandrasekharCentre for Neuroscience, Indian Institute of Science, Bangalore, KA, India.
Byron M YuDepartment of Biomedical Engineering, and Department of Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Devarajan SridharanCentre for Neuroscience, Indian Institute of Science, Bangalore, KA, India. sridhar@iisc.ac.in.ORCID 0000-0003-1998-9018
Indian Institute of Science Bangalore · INCarnegie Mellon University · USUniversity of British Columbia · CA

Funding

Shaping Neural Population Dynamics to Facilitate LearningR01HD071686 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BATISTA, AARON PAUL, CHASE, STEVEN M · 2011 to 2021
$3.7M
Interrogating and Perturbing Neural DynamicsR01NS105318 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Aaron Paul Batista, Steven M Chase · 2017 to 2026
$2.3M
CRCNS: Modulating Neural Population Interactions between Cortical AreasR01MH118929 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI SMITH, MATTHEW A, YU, BYRON M. · 2018 to 2022
$1.7M
Neuronal population dynamics within and across cortical areasR01EB026953 · NIBIB · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DOIRON, BRENT D., SMITH, MATTHEW A · 2018 to 2020
$1.0M
DBT-Wellcome Trust India Alliance IA/I/15/2/502089NIBIB NIH HHS R01 EB026953NICHD NIH HHS R01 HD071686NIMH NIH HHS R01 MH118929NINDS NIH HHS R01 NS105318Wellcome Trust
6 · The paper itself

Abstract

Selective attention produces systematic effects on neural states. It is unclear whether, conversely, momentary fluctuations in neural states have behavioral significance for attention. We investigated this question in the human brain with a cognitive brain-machine interface (cBMI) for tracking electrophysiological steady-state visually evoked potentials (SSVEPs) in real-time. Discrimination accuracy (d') was significantly higher when target stimuli were triggered at high, versus low, SSVEP power states. Target and distractor SSVEP power was uncorrelated across the hemifields, and target d' was unaffected by distractor SSVEP power states. Next, we trained participants on an auditory neurofeedback paradigm to generate biased, cross-hemispheric competitive interactions between target and distractor SSVEPs. The strongest behavioral effects emerged when competitive SSVEP dynamics unfolded at a timescale corresponding to the deployment of endogenous attention. In sum, SSVEP power dynamics provide a reliable readout of attentional state, a result with critical implications for tracking and training human attention.

Indexed as

Brain-Computer InterfacesCognitionHumans

Identifiers

PMID36481698
PMCPMC9732358
OpenAlexW4311575282

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.