fNIRS Acquisition Device for Monitoring Cognitive Load

Accurate and accessible cognitive load monitoring is essential for improving personalized learning and facilitating better mental health monitoring. Existing methods for measuring cognitive load can be intrusive, costly, and less effective for individuals with high melanin concentrations, emphasizing the need for an improved approach. This study presents a novel approach to monitoring the cognitive load using functional near-infrared spectroscopy (fNIRS) technology. A lightweight, cost-effective fNIRS device with a single optode was developed to record prefrontal cortex data, specially designed to suit the skin with high melanin concentration. Data from participants performing n-back tasks revealed significant differences between hemoglobin concentrations among 1-back, 2-back, and 3-back tasks via ANOVA analysis. To further explore cognitive load assessment, various machine learning models were implemented using open-access datasets acquired during n-back tasks. LSTM demonstrated the highest accuracy at 89.55%, suggesting its selection as the best model. The chosen LSTM model was then employed on our recorded dataset, achieving an accuracy of 74.04%. This integration of fNIRS technology and machine learning holds promise for accurate and accessible cognitive load assessment, with potential applications in education, human-computer interaction, and neuroscience research, contributing to a more sustainable future.