ReviewScientific reports2023
An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.
Review in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- From screens to live interactions: Considerations for naturalistic EEG paradigms in social and affective neuroscience.Developmental cognitive neuroscience · 2026Article
- Decoding motor imagery related to major mimetic muscles from electroencephalography.Journal of neuroengineering and rehabilitation · 2026Article
- Inferring Arm Movement Direction from EEG Signals Using Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Video-dominant emotion recognition for portable EEG-based devices.Scientific reports · 2026Article
- Decoding visual object recognition from EEG signals.PloS one · 2026Article
- SegClarity: An Attribution-Based XAI Workflow for Evaluating Historical Document Layout Models.Journal of imaging · 2025Article
- A deep learning-enriched framework for analyzing brain functional connectivity.Scientific reports · 2025Article
- Estimation of Task-Related Dynamic Brain Connectivity via Data Inflation and Classification Model Explainability.Neuroinformatics · 2025Article
- Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.Brain topography · 2025Article
- Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.Scientific reports · 2025Article
- Artificial Intelligence Can Drive Sleep Medicine.Sleep medicine clinics · 2025Review
- Response coupling with an auxiliary neural signal for enhancing brain signal detection.Scientific reports · 2025Article
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Exploration of an intrinsically explainable self-attention based model for prototype generation on single-channel EEG sleep stage classification.Scientific reports · 2024Article
- A generic error-related potential classifier based on simulated subjects.Frontiers in human neuroscience · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.