An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth

• Published in: Scientific reports 2023-10, Vol.13 (1), p.17709-17709, Article 17709
• Main Authors: Sujatha Ravindran, Akshay, Contreras-Vidal, Jose
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/1647/1453/1450; 631/378/116/2394; Deep learning; EEG; Electroencephalography; Humanities and Social Sciences; multidisciplinary; Neural coding; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-43871-8

Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In this study, we compared multiple model explanation methods to identify the most suitable method for EEG and understand when some of these approaches might fail. A simulation framework was developed to evaluate the robustness and sensitivity of twelve back-propagation-based visualization methods by comparing to ground truth features. Multiple methods tested here showed reliability issues after randomizing either model weights or labels: e.g., the saliency approach, which is the most used visualization technique in EEG, was not class or model-specific. We found that DeepLift was consistently accurate as well as robust to detect the three key attributes tested here (temporal, spatial, and spectral precision). Overall, this study provides a review of model explanation methods for DL-based neural decoders and recommendations to understand when some of these methods fail and what they can capture in EEG.