Measuring Cognitive Load: Leveraging fNIRS and Machine Learning for Classification of Workload Levels

Measuring cognitive load, a subjective construct that reflects the mental effort required for a given task, remains a challenging endeavor. While Functional Near-Infrared Spectroscopy (fNIRS) has been utilized in the field of neurology to assess cognitive load, there are limited studies that have specifically focused on high cognitive load scenarios. Previous research in the field of cognitive workload assessment using fNIRS has primarily focused on differentiating between two levels of mental workload. These studies have explored the classification of low and high levels of cognitive load, or easy and difficult tasks, using various Machine Learning (ML) and Deep Learning (DL) models. However, there is a need to further investigate the detection of multiple levels of cognitive load to provide more fine-grained information about the mental state of an individual. This study aims to classify four mental workload levels using classical ML techniques, specifically random forests, with fNIRS data. It assesses the effectiveness of ML algorithms with fNIRS data, provides insights into classification features and patterns, and contributes to understanding neural mechanisms in cognitive processing. ML algorithms used for classification include Naïve Bayes, k-Nearest Neighbors (k-NN), Decision Trees, Random Forests, and Nearest Centroid. Random Forests achieved a promising accuracy and Area Under Curve (AUC) of around 99.99%. The findings of this study highlight the potential of utilizing fNIRS and ML algorithms for accurately classifying cognitive workload levels. The use of multiple features extracted from fNIRS data may contribute to a more robust and reliable classification approach.