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A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2018 (English)In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 15, no 3, article id 036021Article in journal (Refereed) Published
Abstract [en]

Objective. Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Approach. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, n is an element of [1, 2]. Main results. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-bytrial working memory task performance is demonstrated (r = 0.47, p < 0.05). It is furthermore shown that this measure allows to predict task performance before action (r = 0.49, p < 0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. Significance. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain-machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.

Place, publisher, year, edition, pages
IOP PUBLISHING LTD , 2018. Vol. 15, no 3, article id 036021
Keywords [en]
EEG, working memory, decoding, task performance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-39074DOI: 10.1088/1741-2552/aaae73ISI: 000429340600003PubMedID: 29623902Scopus ID: 2-s2.0-85047467115OAI: oai:DiVA.org:mdh-39074DiVA, id: diva2:1201572
Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2018-06-07Bibliographically approved

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Åstrand, Elaine

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