Cognitive task and workload classification using EEG signal

Abstract

Cognitive workload is defined as the load generated within our brain as a result of cognitive and psychological effort which is given to accomplish the desired task. Analysis of mental workload and cognitive stress monitoring is extremely crucial as it directly impacts humans' performance in executing the tasks as well as in mental health. Recent studies in neuroscience have differentiated various cognitive states by analyzing local changes in neuronal activity in human brain. Given cost-effectiveness and high sensitivity to detect brain activity fluctuations for varieties of cognitive tasks, electroencephalography (EEG) analysis has been widely studied by researchers in order to understand the information about brain functionality that EEG signal contains. Here we present a comprehensive overview on how EEG can be utilized to identify different cognitive states for assessing mental workload. A systematic review of the literature was performed on EEG signal for different mental stress tasks, where the subjects undergo with varying degrees of task complexity. In the present study, the results are analyzed based on the type of tasks, different EEG preprocessing methods and classification models. The study summarizes the recent practices and performance outcomes for mental workload classification using EEG signal.