A multilayer deep neural network framework for hemodynamic assessment of cognitive load management during problem-solving tasks.

Abstract

Cognitive load refers to the mental effort required to process information and perform tasks, significantly influencing learning and performance outcomes. This paper presents a novel approach for cognitive load classification using a hybrid model that integrates Long Short-Term Memory (LSTM) networks with the Block Attention Module (BAM). Leveraging functional Near-Infrared Spectroscopy (fNIRS), we investigate the relationship between cognitive load and brain activity in a controlled experimental setting. Our methodology encompasses data collection from 50 participants engaged in various problem-solving tasks, with cognitive load categorized as high, medium, or low. The acquired fNIRS data underwent a rigorous preprocessing pipeline, including normalization and wavelet transform for feature extraction, enabling a comprehensive analysis of hemodynamic responses. The proposed model employs BAM to enhance feature representation by refining the importance of spatial and channel dimensions, thus improving the LSTM's ability to capture temporal dependencies in the data. The experimental results demonstrate significant performance improvements in cognitive load classification, showcasing the efficacy of the integrated LSTM-BAM architecture. This work not only contributes to the understanding of cognitive load dynamics but also highlights the potential of fNIRS as a non-invasive tool for real-time monitoring of cognitive performance, paving the way for advancements in instructional design and cognitive research.