Cognitive load (CL) refers to the amount of mental demand imposed on a user by a particular task, and has been associated with the limited capacity of working memory and the ability to process novel information. High levels of CL are known to decrease effectiveness and performance of users, as well as their ability to learn from their tasks. While a low level of CL may cause boredom and lack of situational awareness. This project aims at investigating approaches for measuring an individual’s CL levels in real-time unobtrusively and automatically. Multiple measures, including physiological (e.g. skin conductance, pupil dilation and EEG) and behavioural patterns (e.g. speech/language, eye-movement, mouse movement, and pen-gesture/handwriting) have been being investigated in different task contexts and scenarios, to dynamically assess the level of load a user experiences while performing a task. Factors which affect cognitive load measurement (CLM) such as stress, trust, and environmental factors such as illumination are also extensively studied in this project. Furthermore, dynamic workload adjustment and real-time CLM with data streaming are evaluated to make CLM accessible by more widespread applications and users.
BrainGauge© (https://www.braingauge.com.au/), the software product for cognitive load measurement based on our research outcomes has been developed and applied in practical applications.
People: Fang Chen, Jianlong Zhou, Kun Yu, Ahmad Khawaji, Syed Arshad, Yang Wang