Chirplet transform-based machine-learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

Recent advances in the field of cognitive sciences have provided evidence to classify activities with different cognitive engagement levels. High cognitive load and sustained attention may predispose an individual to psychological and physical stress and may result in a lack of performance. Cognitive load monitoring of professionals in high precision fields has gathered significant attention in the current research scenario. The proposed work in continuity has tried to demonstrate the use of two non-invasive physiological sensing modalities viz. galvanic skin response (GSR) and photoplethysmography (PPG) to elucidate effectiveness in picking up differences of two induced cognitive state categories based on machine learning and statistical methods. This paper demonstrates how GSR and PPG signals data can be effectively utilized to examine the shift in cognitive load. The present work proposes the use of general linear chirplet transform (GLCT) to evaluate the time-frequency characteristic of GSR and PPG signals and utilize the statistical features in classification. Random Forest, Decision Tree, and k-Nearest Neighbours demonstrated an accuracy of 92.13%, 88.0%, and 86.13% respectively on a dataset of 20 subjects against an optimized feature set, thus demonstrating the effectiveness of the proposed methodology for differentiating pre-defined categories of cognitive load. The study shows the potential of GSR and PPG signals attributes and time-frequency representation using GLCT to monitor cognitive load in real-life conditions. The proposed work also indicates the possibility to extend the same to attention-demanding fields such as aviation, medicine, and manufacturing for effective remediation of fatigue periods with increased cognitive demand, which, if sustained, may lead to cognitive decline in the long run.

Automated summary

Recent advances in cognitive sciences have allowed for the classification of activities based on different levels of cognitive engagement. This study utilizes non-invasive physiological sensing techniques, such as galvanic skin response (GSR) and photoplethysmography (PPG), to monitor and classify cognitive load in professionals. The results demonstrate the effectiveness of using GSR and PPG signals along with machine learning and statistical methods to differentiate between different categories of cognitive load. The potential for monitoring cognitive load in real-life conditions is also highlighted, with implications for fields such as aviation, medicine, and manufacturing.