Discrimination of Mental Workload Levels From Multi-Channel fNIRS Using Deep Leaning-Based Approaches

Abstract

Functional near-infrared spectroscopy (fNIRS), known as a non-invasive optical neuroimaging technique, is currently used to assess brain dynamics during the performance of complex works and everyday tasks. However, the deep learning approaches to distinguish stress levels based on the changes in hemoglobin concentrations have not yet been extensively investigated. In this paper, we evaluated the efficiencies of advanced methods differentiating the rest and task periods during Stroop task experiments. We first explored that the apparent changes of oxy-hemoglobin and deoxy-hemoglobin concentrations associated with two mental stages did exist across each participant. The preprocessing steps, such as converting raw signals into hemoglobin values and filtering to remove various noises involved in fNIRS signals, were performed to obtain the clean dataset, called non-PCA inputs. Next, we applied the principal component analysis (PCA) algorithm to get PCA-inputs before putting non-PCA inputs into our four classifiers. Then, a novel deep learning-based discrimination framework was studied. The conventional machine learning algorithms, including SVM and AdaBoost, produced the best accuracies of 64.74% +/- 1.57% and 71.13% +/- 2.96%, respectively. In comparison, the deep learning approaches, including deep belief network and convolutional neural network models, have enabled better classification accuracies of 84.26% +/- 2.58% and 72.77% +/- 1.92%, respectively.