Assessment of mental workload using XGBoost classifier from optimised EEG features. (3rd February 2023)

• Record Type: Journal Article
• Authors: Vani, R.K. Kapila; Padmanabhan, Jayashree
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 14:Number 2(2023)
• Page Start: 109
• Page End: 115
• Publication Date: 2023-02-03
• Subjects: XGBoost -- grey wolf optimiser -- GWO -- STEW dataset; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Cognitive workload evaluation is vital in any critical working environment for assessing the user's mental status. Despite the fact that there are many methods for evaluating cognitive strain, the model that uses electroencephalography (EEG) data remains the most promising one. Brain related activities can be used to assess various mental states and also help us to determine mental effort. This study calculates the cognitive workload of people while performing multitasking mental tasks. Here the 'STEW' dataset is used to measure mental effort. 'No task' and 'simultaneous capacity (SIMKAP)-based multitasking activity' are the two tasks in the dataset. For the study we have chosen only the SIMKAP task dataset. The cognitive workload assessment from optimised EEG features using XGBoost classifier (CWAOEX) framework is proposed, in which numerous features from EEG brain signals are retrieved and the grey wolf optimiser (GWO) is utilised to select the best ones. The data is then categorised according to the best feature set. The XGBoost algorithm is employed in the classification step. The recommended method has the classification accuracy of 94.25 in categorising the workload as low, moderate and high which is better than the current methods.