Explainable artificial intelligence model to predict brain states from fNIRS signals - Shibu, Caleb Jones; Sreedharan, Sujesh; Arun, KM; Kesavadas, Chandrasekharan; Sitaram, Ranganatha (Frontiers in Human Neuroscience, 2023)
Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. This work aims to develop an explainable artificial intelligence (xAI) system that can decompose the Deep Learning model's output onto the input variables. We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN) and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects, and (b) Motor imagery (MI), acquired from twenty-nine subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- and deoxy-hemoglobin levels in those locations. The xAI-fNIRS system can distinguish between the brain states related to overt and covert movements from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.