Evidence map›Paper›PMID 29385839›Full record

ArticleDisability and rehabilitation. Assistive technology2019

Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

Jonathan S Brumberg, Anh Nguyen, Kevin M Pitt, Sean D Lorenz

Open access · greenAbstract read
In one paragraph

Article in Disability and rehabilitation. Assistive technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 26% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Human visual skills for brain-computer interface use: a tutorial.Disability and rehabilitation. Assistive technology · 2020
    Review
  5. Article
  6. Article
  7. Article
  8. Review
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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 at 2 institutions in 1 country.

Jonathan S Brumberga Department of Speech-Language-Hearing: Sciences & Disorders , University of Kansas , Lawrence , KS , USA.ORCID 0000-0001-5739-968X
Anh Nguyenb Department of Speech Language & Hearing Sciences , College of Health & Rehabilitation Sciences: Sargent College, Boston University , Boston , MA , USA.
Kevin M Pitta Department of Speech-Language-Hearing: Sciences & Disorders , University of Kansas , Lawrence , KS , USA.
Sean D Lorenzc Center for Computational Neuroscience and Neural Technology , Boston University , Boston , MA , USA.
Boston University · USUniversity of Kansas · US

Funding

Using PCORnet to Expand the DS-CONNECT Cohort Through Healthcare System Recruitment, Incorporating Electronic Health Records, and Assessing Self-DeterminationU54HD090216 · NICHD · UNIVERSITY OF KANSAS LAWRENCE · PI HOFFMAN, LESA R · 2016 to 2020
$6.2M
Investigating output modality for a brain-computer interface for communicationR03DC011304 · NIDCD · UNIVERSITY OF KANSAS LAWRENCE · PI BRUMBERG, JONATHAN S · 2011 to 2013
$439k
NICHD NIH HHS U54 HD090216NIDCD NIH HHS R03 DC011304
6 · The paper itself

Abstract

purposeWe investigated how overt visual attention and oculomotor control influence successful use of a visual feedback brain-computer interface (BCI) for accessing augmentative and alternative communication (AAC) devices in a heterogeneous population of individuals with profound neuromotor impairments. BCIs are often tested within a single patient population limiting generalization of results. This study focuses on examining individual sensory abilities with an eye toward possible interface adaptations to improve device performance.

methodsFive individuals with a range of neuromotor disorders participated in four-choice BCI control task involving the steady state visually evoked potential. The BCI graphical interface was designed to simulate a commercial AAC device to examine whether an integrated device could be used successfully by individuals with neuromotor impairment.

resultsAll participants were able to interact with the BCI and highest performance was found for participants able to employ an overt visual attention strategy. For participants with visual deficits to due to impaired oculomotor control, effective performance increased after accounting for mismatches between the graphical layout and participant visual capabilities.

conclusionAs BCIs are translated from research environments to clinical applications, the assessment of BCI-related skills will help facilitate proper device selection and provide individuals who use BCI the greatest likelihood of immediate and long term communicative success. Overall, our results indicate that adaptations can be an effective strategy to reduce barriers and increase access to BCI technology. These efforts should be directed by comprehensive assessments for matching individuals to the most appropriate device to support their complex communication needs. Implications for Rehabilitation Brain computer interfaces using the steady state visually evoked potential can be integrated with an augmentative and alternative communication device to provide access to language and literacy for individuals with neuromotor impairment. Comprehensive assessments are needed to fully understand the sensory, motor, and cognitive abilities of individuals who may use brain-computer interfaces for proper feature matching as selection of the most appropriate device including optimization device layouts and control paradigms. Oculomotor impairments negatively impact brain-computer interfaces that use the steady state visually evoked potential, but modifications to place interface stimuli and communication items in the intact visual field can improve successful outcomes.

Indexed as

Brain-Computer InterfacesAdultAgedAttentionBrain DiseasesEvoked Potentials, VisualEye MovementsFeedback, SensoryFemaleHumansMaleMiddle AgedTask Performance and AnalysisUser-Computer InterfaceAACBrain-computer interface (BCI)feature matchingSSVEPsteady-state visually evoked potential

Identifiers

PMID29385839
PMCPMC6068003
OpenAlexW2793714248

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.