Detecting cognitive traits and occupational proficiency using EEG and statistical inference

Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remai...

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Bibliographic Details
Published inScientific reports Vol. 14; no. 1; pp. 5605 - 12
Main Authors Mikheev, Ilya, Steiner, Helen, Martynova, Olga
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 07.03.2024
Nature Publishing Group
Nature Portfolio
Subjects
631/378/116/1925
631/378/2649/1725
Classification
Cognition
Cognition & reasoning
Cognitive ability
Cognitive traits
Education
EEG
Electroencephalography
Electroencephalography - methods
Humanities and Social Sciences
Humans
Machine Learning
Mathematics
multidisciplinary
Neurological diseases
Science
Science (multidisciplinary)
EEG
Cognitive traits
Machine learning
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Summary:Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remains challenging. This study compared the subject-independent classification performance of three different pipelines: supervised and Riemann projections with logistic regression and handcrafted power spectral features with light gradient boosting machine (LightGBM). 128-channel EEGs were recorded from 26 healthy volunteers while they solved arithmetic, logical, and verbal tasks. The participants were divided into two groups based on their higher education and occupation: specialists in mathematics and humanities. The balanced accuracy of the education type was significantly above chance for all pipelines: 0.84–0.89, 0.85–0.88, and 0.86–0.88 for each type of task, respectively. All three pipelines allowed us to distinguish mathematical proficiency based on learning experience with different trade-offs between performance and explainability. Our results suggest that ML approaches could also be effective for recognizing individual cognitive traits using EEG.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-55163-w