Explainable Deep Learning for Brain-Computer Interfaces through Layerwise Relevance Propagation

With an ever-growing demand for BCI-based systems, numerous algorithms and machine learning systems have been proposed over the past few decades. Although state-of-theart approaches have reached practically appropriate levels of accuracy, most are often regarded as black-box models, which need more explainability. However, for cases where a Neural Network is used as the base for a classifier, a Layerwise Relevance Propagation (LRP) approach can be utilized to analyze the decision boundaries considered by the network. By calculating the importance of the neuron in each layer, the LRP can also be used as an effective model complexity reduction technique through the inactivation (pruning) of the neural pathways. The following work investigates the usability of the LRP framework in the field of BCI. This study provides an example of the practical application of the LRP with respect to the EEG (ERP) dataset, along with visual heatmap and scalp map examples of the LRP. Furthermore, the work analyzes the impact of network pruning on heatmap visualization and the model's accuracy while also practically determining the maximum cutoff range for pruning BCI models.