Evidence map›Paper›PMID 29614297›Full record

ArticleJournal of neuroscience methods2018

EEG-based neglect assessment: A feasibility study.

Aya Khalaf, Jessica Kersey, Safaa Eldeeb, Gazihan Alankus, Emily Grattan, Laura Waterstram, Elizabeth Skidmore, Murat Akcakaya

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroscience methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Aya KhalafElectrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA 15213, USA. Electronic address: afk17@pitt.edu.
Jessica KerseyDepartment of Occupational Therapy, University of Pittsburgh, 5012 Forbes Tower, Pittsburgh, PA 15260, USA.
Safaa EldeebElectrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA 15213, USA.
Gazihan AlankusDepartment of Computer Engineering, Izmir University of Economics, Sakarya Cd. 156, 35330, Turkey.
Emily GrattanDepartment of Health Sciences and Research, Medical University of South Carolina, 77 President Street, MSC 700, Charleston, SC 29425, USA.
Laura WaterstramDepartment of Occupational Therapy, University of Pittsburgh, 5012 Forbes Tower, Pittsburgh, PA 15260, USA.
Elizabeth SkidmoreDepartment of Occupational Therapy, University of Pittsburgh, 5012 Forbes Tower, Pittsburgh, PA 15260, USA.
Murat AkcakayaElectrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA 15213, USA.

Funding

Treating Depression and Enhancing Locomotor Recovery Post-strokeP20GM109040 · NIGMS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI KAUTZ, STEVEN A. · 2014 to 2024
$23.5M
NIGMS NIH HHS P20 GM109040
6 · The paper itself

Abstract

backgroundSpatial neglect (SN) is a neuropsychological syndrome that impairs automatic attention orienting to stimuli in the contralesional visual space of stroke patients. SN is commonly assessed using paper and pencil tests. Recently, computerized tests have been proposed to provide a dynamic assessment of SN. However, both paper- and computer-based methods have limitations. NEW

methodElectroencephalography (EEG) shows promise for overcoming the limitations of current assessment methods. The aim of this work is to introduce an objective passive BCI system that records EEG signals in response to visual stimuli appearing in random locations on a screen with a dynamically changing background. Our preliminary experimental studies focused on validating the system using healthy participants with intact brains rather than employing it initially in more complex environments with patients having cortical lesions. Therefore, we designed a version of the test in which we simulated SN by hiding target stimuli appearing on the left side of the screen so that the subject's attention is shifted to the right side.

resultsResults showed that there are statistically significant differences between EEG responses due to right and left side stimuli reflecting different processing and attention levels towards both sides of the screen. The system achieved average accuracy, sensitivity and specificity of 74.24%, 75.17% and 71.36% respectively. COMPARISON WITH EXISTING

methodsThe proposed test can examine both presence and severity of SN, unlike traditional paper and pencil tests and computer-based methods.

conclusionsThe proposed test is a promising objective SN evaluation method.

Indexed as

Signal Processing, Computer-AssistedAdultAttentionCerebral CortexDiagnosis, Computer-AssistedElectroencephalographyFeasibility StudiesHumansPerceptual DisordersSensitivity and SpecificitySpace PerceptionStrokeAttentionClassificationComputer-aided diagnosisElectroencephalographySpatial neglectStroke

Identifiers

PMID29614297
PMCPMC7156006

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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.