Extracting Interpretable EEG Features from a Deep Learning Model to Assess the Quality of Human-Robot Co-manipulation

There is an increasing interest in adapting the deep learning models into neuroimaging techniques such as electroencephalogram (EEG). However, one of the fundamental problems in deep learning models is the interpretability of the learned representations. Even though many interpretability models exist for computer vision applications, adapting those methods for deep learning using EEG is still a challenge. In this regard, we propose a novel computational approach to increase the interpretability of results from deep learning algorithm using two popular saliency detection algorithms: integrated gradients and ablation attribution method. The method provides the importance of values across different EEG frequency bands (Theta, Alpha, Beta, Gamma) and across different electrode locations. We can use these importance values to recognize which electrode and frequency bands are relevant for a particular classification problem. We demonstrate the proposed method's efficacy in a physical human-robot co-manipulation experiment where a convolution neural network (CNN) model is trained to classify the user's mental workload using raw EEG recordings. The experiment is predominantly visuospatial and motor control-oriented. The proposed method found the Gamma and Beta frequency band across parietal and occipital regions to be important, which are indeed associated with visuospatial processing and sensory integration.