Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces

TL;DR

This paper evaluates various deep learning interpretation techniques for EEG-based BCI, highlighting the importance of proper method selection and proposing procedures to improve trustworthiness and understandability of interpretation results.

Contribution

It provides a comprehensive quantitative evaluation of interpretation methods for EEG models and introduces procedures to enhance the reliability of interpretation outcomes.

Findings

Interpretation quality varies across samples despite overall method performance.

Model structure and dataset type influence interpretation accuracy.

Proper interpretation technique selection is crucial for trustworthy explanations.

Abstract

As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. In order to fill this research gap, we conduct a study to evaluate different deep interpretation techniques quantitatively on EEG datasets. The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.