Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification

Abstract

Objective. Understanding the cognitive load of drivers is crucial for road safety. Brain sensing has the potential to provide an objective measure of driver cognitive load. We aim to develop an advanced machine learning framework for classifying driver cognitive load using functional near-infrared spectroscopy (fNIRS). Approach. We conducted a study using fNIRS in a driving simulator with the N-back task used as a secondary task to impart structured cognitive load on drivers. To classify different driver cognitive load levels, we examined the application of convolutional autoencoder (CAE) and Echo State Network (ESN) autoencoder for extracting features from fNIRS. Main results. By using CAE, the accuracies for classifying two and four levels of driver cognitive load with the 30 s window were 73.25% and 47.21%, respectively. The proposed ESN autoencoder achieved state-of-art classification results for group-level models without window selection, with accuracies of 80.61% and 52.45% for classifying two and four levels of driver cognitive load. Significance. This work builds a foundation for using fNIRS to measure driver cognitive load in real-world applications. Also, the results suggest that the proposed ESN autoencoder can effectively extract temporal information from fNIRS data and can be useful for other fNIRS data classification tasks.