Revealing the Electrophysiological Correlates of Working Memory-Load Effects in Symmetry Span Task With HHT Method

Abstract

Complex span task is one of the commonly used cognitive tasks to evaluate an individual’s working memory capacity (WMC). It is a dual task consisting of a distractor subtask and a memory subtask. Though multiple studies have utilized complex span tasks, the electrophysiological correlates underlying the encoding and retrieval processes in working memory span task remain uninvestigated. One previous study that assessed electroencephalographic (EEG) measures utilizing complex span task found no significant difference between its working memory loads, a typical index observed in other working memory tasks (e.g., n-back task and digital span task). The following design constructs of the paradigm might have been the reason. (1) The fixed-time limit of the distractor subtask may have hindered the assessment of individual WMC precisely. (2) Employing a linear-system-favoring EEG data analysis method for a non-linear system such as the human brain. In the current study, the participants perform the Raven Advanced Progressive Matrices (RAMP) task on 1 day and the symmetry span (Sspan) task on the other. Prior to the formal Sspan task, the participants were instructed to judge 15 simple symmetry questions as quickly as possible. A participant-specific time-limit is chartered from these symmetry questions. The current study utilizes the Sspan task sequential to a distractor subtask. Instead of the fixed time-limit exercised in the previous study, the distractor subtask of the current study was equipped with the participant-specific time-limit obtained from the symmetry questions. This could provide a precise measure of individual WMC. This study investigates if the complex span task resonates EEG patterns similar to the other working memory tasks in terms of working memory-load by utilizing ensemble empirical mode decomposition (EEMD) of Hilbert-Huang transform (HHT). Prior expectations were to observe a decrement in the P300 component of event-related mode (ERM) and a decrement in the power of alpha and beta band frequency with increasing working memory-load. We observed a significantly higher P300 amplitude for the low-load condition compared to the high-load condition over the circumscribed brain network across F4 and C4 electrodes. Time–frequency analysis revealed a significant difference between the high- and low-load conditions at alpha and beta band over the frontal, central, and parietal channels. The results from our study demonstrate precise differences in EEG data pertaining to varied memory-load differences in the complex span task. Thus, assessing complex span tasks with the HHT-based analysis may aid in achieving a better signal to noise ratio and effect size for the results in working memory EEG studies.

To Go or Not to Go: Degrees of Dynamic Inhibitory Control Revealed by the Function of Grip Force and Early Electrophysiological Indices

“…Several recent studies have applied event-related mode (ERM), an improved method for measuring ERP, to precisely explore task-related components (Williams et al, 2011 ; Al-Subari et al, 2015 ; Chang et al, 2016 ; Hsu et al, 2016 ; Chuang et al, 2019 ). ERM is based on empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD; Wu and Huang, 2009 ; Wu et al, 2009 ).…”

Complementary Correlational Profiles between Slow Wave Brain Oscillations and Working Memory Capacity

“…Scharinger and colleagues found a decrease in alpha and an increase in theta brain wave at fronto-central and parieto-occipital during performance of the operation span task [17]. This was also observed in other working memory tasks [18,19]. Increasing demands on working memory have been found to increase alpha band power, but most of these studies measure the EEG when the participants are engaged in processing a task, varying the working memory demand [20].…”

Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

“…A novel fully data-driven method (Huang et al 1998) for the analysis of non-Gaussian, nonlinear and non-stationary signals, the empirical mode decomposition (EMD), followed by the Hilbert transform of the extracted modal components with the EMD, known also as the Hilbert-Huang transform method, has been introduced for the study of EEG signals (Al-Subari et al 2015;Al-Subari et al 2016;Carella et al 2018;Chatterjee 2019;Chuang et al 2019;Dinares-Ferran et al 2018;Estevez-Baez et al 2017a, b, c;Hansen et al 2019;Hassan, Bhuiyan 2017;Hou et al 2018;Javed et al 2019;Rahman, Fattah 2017). The modal components extracted using the EMD can be considered as an adaptive spectral band (Noshadi et al 2014).…”