Cognitive Load and Neuroplasticity in Multitasking: Insights from EEG-Based Brain Activity Mapping

Abstract

Multitasking has risen as one of the digital behaviour characteristics defining how people work with information, how they distribute attention, and how they cognitively adjust to multitask conditions. Although it is widespread, the specific neural mechanism by which multitasking determines cognitive load and triggers neuroplastic transformations has not been studied properly. The present study offers a detailed examination of the high-resolution neuroplastic adaptations of the brain activities of neural efficiency and cognitive load interdependence in relation to the effects of neural activation variables of the high-resolution electroencephalography (EEG) brain activity mapping. An analytical framework across time frequency decomposition, spectral power analysis, functional connectivity modelling and machine learning-based cognitive load classification were used to elicit finer grains neural proxies of intensifying multitasking load. According to the experimental findings, there is a significant increase in frontal theta and beta oscillation, which is symptomatic of increased working memory levels, attentional control and executive processing loads due to increased levels of multitasking. Also, there are an increase in fronto-parietal connexion and a decrease in parietal-occipital alpha activity suggesting more cross-regional coordination needed to serve multiple streams of tasks and more perceptual gating and sensory processing. Longitudinal studies also indicate neuroplastic changes, which are marked with persistent changes in baseline spectral power, enhanced adaptability in the modularity of the fronto-parietal networks, and enhanced neural efficiency on repeated exposure to multitasking settings. Classifiers built using information extracted with EEG features were high-ranking in distinguishing between cognitive states of load, emphasising the plausibility of automated systems to track state of cognitive functions in real-time. Together, these results have essential implications regarding the way in which the brain reconfigures and optimises neural circuits during continued multitasking pressure which can be used to depict more insight into cognitive neuroscience, neuroergonomics, human-computer interaction, and the design of workload-sensitive neuroadaptive systems. The study is also the foundation of the development of customised cognitive training regimes, improved adaptive user interfaces, as well as training the strategies to reduce cognitive fatigue in multitasking high-challenge environments.