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Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults

Overview of attention for article published in Neuropsychiatric Electrophysiology, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

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18 X users
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1 peer review site
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2 Redditors

Citations

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111 Dimensions

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204 Mendeley
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Title
Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults
Published in
Neuropsychiatric Electrophysiology, February 2016
DOI 10.1186/s40810-016-0017-0
Pubmed ID
Authors

Jason K. Johannesen, Jinbo Bi, Ruhua Jiang, Joshua G. Kenney, Chi-Ming A. Chen

Abstract

With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically. Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses. SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function. EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.

X Demographics

X Demographics

The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 204 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Mexico 1 <1%
Netherlands 1 <1%
France 1 <1%
Unknown 200 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 21%
Student > Master 30 15%
Researcher 25 12%
Student > Bachelor 18 9%
Student > Doctoral Student 15 7%
Other 23 11%
Unknown 51 25%
Readers by discipline Count As %
Neuroscience 32 16%
Engineering 32 16%
Psychology 25 12%
Computer Science 24 12%
Medicine and Dentistry 9 4%
Other 16 8%
Unknown 66 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 September 2016.
All research outputs
#3,139,475
of 24,078,222 outputs
Outputs from Neuropsychiatric Electrophysiology
#5
of 19 outputs
Outputs of similar age
#56,386
of 408,500 outputs
Outputs of similar age from Neuropsychiatric Electrophysiology
#2
of 2 outputs
Altmetric has tracked 24,078,222 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 19 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one scored the same or higher as 14 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 408,500 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.