Deep Learning Forecast of Perceptual Load Using fNIRS Data

Abstract

In this paper, we present a novel approach to forecasting perceptual load in demanding piloting tasks, based on neurophysiological response data. We introduce a forecasting framework using a multinomial classification model paired with deep learning sequence-to-sequence models. The study compared the performance of seven different deep learning models, including GRU, Transformer, and linear models with a 10s outlook against a statical model benchmark. For analysis and validation purposes, the dataset was first split into training and testing sets, and the training set was further used to perform a 5-fold cross validation. The cross-validation results were used to evaluate generalization in terms of the regression loss used to train the deep learning models, while the testing set was used to evaluate the classification performance, including macro and weighted recall, precision and F1 scores. The prediction time for each model (computational demand) was also analysed for insight into model viability for real-time perceptual load forecasting.

Keywords: Perceptual load, Forecasting, Deep learning, fNIRS, Human performance monitoring

DOI: 10.54941/ahfe1005563