Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
📰 ArXiv cs.AI
Learn to compare post-hoc explainable AI methods for interpreting black-box EEG models in depression detection using Shapley-based, gradient-based, and perturbation-based techniques
Action Steps
- Apply Shapley-based methods to assign feature importance in EEG models
- Run gradient-based explainability methods to analyze model decisions
- Configure perturbation-based techniques to test model robustness
- Compare the results of different explainability methods to select the most effective approach
- Test the selected method on a holdout dataset to evaluate its performance
Who Needs to Know This
Data scientists and AI engineers working on healthcare projects can benefit from this knowledge to improve model interpretability and transparency
Key Insight
💡 Post-hoc explainability methods can be used to interpret black-box EEG models and improve model transparency in depression detection
Share This
🤖 Improve model interpretability in EEG-based depression detection using post-hoc explainable AI methods! 📊
Key Takeaways
Learn to compare post-hoc explainable AI methods for interpreting black-box EEG models in depression detection using Shapley-based, gradient-based, and perturbation-based techniques
Full Article
Title: Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
Abstract:
arXiv:2605.28977v1 Announce Type: cross Abstract: Recent advances in deep learning have enabled increasingly accurate electroencephalography (EEG)-based classification of Major Depressive Disorder (MDD), but the decision-making processes of high-capacity models remain difficult to interpret. This study investigates multiple post-hoc explainability methods applied to an InceptionTime architecture trained for EEG-based MDD detection. The analysis includes Shapley-based, gradient-based, and perturb
Abstract:
arXiv:2605.28977v1 Announce Type: cross Abstract: Recent advances in deep learning have enabled increasingly accurate electroencephalography (EEG)-based classification of Major Depressive Disorder (MDD), but the decision-making processes of high-capacity models remain difficult to interpret. This study investigates multiple post-hoc explainability methods applied to an InceptionTime architecture trained for EEG-based MDD detection. The analysis includes Shapley-based, gradient-based, and perturb
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