Real time detection of cognitive load using fNIRS: A deep learning approach

Abstract

Functional near infrared spectroscopy (fNIRS) is a non-invasive tool for monitoring functional brain activation that records changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations. fNIRS is well accepted in the cognitive study where the signals are intended to measure cognitive load in the human brain. Concentration changes in HbO and HbR help in classifying the cognitive states of human brain. There are several machine learning classification techniques to distinguish different cognitive states. Some conventional machine learning methods, which are easier to implement, undergo a complex processing phase before training the network and also suffer from low accuracy due to inappropriate data preprocessing. Deep learning based convolutional neural network (CNN) having automatic feature engineering capability plays a very important role in efficiently classifying different cognitive states. The present work uses two open-access datasets on fNIRS signal. The datasets are taken for two cognitive states: mental task (MT) and resting state or baseline task (BL). The concentration changes of HbO and HbR are computed using the modified Beer–Lambert law. The band-pass filter is used to remove additional noise from the signals. Here, topographical brain images are generated from the data of 2 s window with 1 s overlapping for both HbO and HbR. Global normalization is applied to the filtered data for better visualization of the images. The brain images are fed to the proposed CNN model in order to classify them into MT or BL. The accuracy of the classification and the comparative study shows the superiority of the proposed model over two existing models.