EEG-based brain connectivity analysis of working memory and attention

Abstract

Recent research reveal that the Working Memory (WM) is more powerful than IQ as a predictor of academic success. However, there are factors that may influence WM performance, such as Attention. Although the impact of attention is well documented using ERPs; yet, the underlying brain connectivity of the interaction of these two constructs is not sufficiently understood. In this study, a Delay-Response task and electroencephalography (EEG) data are used to investigate the brain connectivity during two stages of Working Memory: Encoding and Maintenance. We have presented distraction in both stages, and a secondary task in maintenance stage. Scalp EEG data of 19 participants were recorded. These results not only reveal the underlying brain connectivity of each task, but also highlights the differences between distraction and multitasking. The results show significant brain connectivity changes in the frontal and occipital areas of the brain depending on the WM stage where the distraction is presented.

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

“…With responses time-locked to the stimuli, the participants demonstrated significantly higher activation in RF when compared to others in the frontal lobe, a region clearly associated with working memory systems and cognitive processing. In agreement with several neuroimaging studies [55][56][57], the brain activation increased in the frontal area. In addition, a combination of all considerations suggests that simultaneous EEG and fNIRS should be preferred to only EEG or fNIRS.…”

A tEEG framework for studying designer’s cognitive and affective states

“…(Wang et al, 2014) Mental stress. (Al-shargie et al, 2016) 22/47 surveyed cognitive studies (Table 5), scholars try to identify patterns of the extracted features regarding to a certain level of their studied cognitive problem (Fink et al, 2009;Bashiri et al, 2015;Hanouneh et al, 2016). It may be concluded that EEG-based cognitive findings are somehow still at the early stage as specific cognitive states are neither recognized nor quantified in the surveyed studies.…”

“…Table 4 shows that scholars are now able to recognize or quantify some emotional states from EEG signals, even though the number and taxonomy of the EEG-based emotional state recognition are still diversified. The listed affective recognition results are represented either in a two-dimensional (2D) (valence-arousal) model (Frantzidis et al, 2010; Rozgić et al, 2013; Hadjidimitriou et al, 2015) or as discrete emotional states (Bar-Haim et al, 2005; Murugappan et al, 2010; Liu et al, 2011), whereas in most of the surveyed cognitive studies (Table 5), scholars try to identify patterns of the extracted features regarding to a certain level of their studied cognitive problem (Fink et al, 2009; Bashiri et al, 2015; Hanouneh et al, 2016). It may be concluded that EEG-based cognitive findings are somehow still at the early stage as specific cognitive states are neither recognized nor quantified in the surveyed studies.…”

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

“…These methods, while advanced, often emphasize the local or regional properties (regions of interest) of the brain activity, potentially ignoring the global characteristics. This focus on specific areas may lead to a limited understanding of the complex and dynamic interactions that occur across all brain regions while performing a given cognitive task [18], [19]. Assessing such interactions provides relevant information about brain functioning between disease and control groups.…”