Interpret CNN classification model for EEG signals.

I have a CNN model for EEG signals classification, I built the model, train and test it. I want to interpret the decision-making process of the CNN model , How can I do that ? Should I use on of the attached methodes?

Answer 1

[arushi]

Hi Rabeah,

Here are several methods you can use to interpret your CNN model:

1. Visualization Techniques

a. Saliency Maps - Saliency maps highlight the parts of the input that are most important for the CNN's decision. In MATLAB, you can use the `deepDreamImage` function to visualize the activations of different layers.

b. Grad-CAM (Gradient-weighted Class Activation Mapping) - Grad-CAM provides a coarse localization map highlighting the important regions in the input. MATLAB does not have a direct function for Grad-CAM, but you can implement it using the gradient of the output with respect to the feature maps.

2. Feature Importance

a. Permutation Feature Importance - This method involves shuffling the values of each feature and measuring the change in model performance.

3. Layer-wise Relevance Propagation (LRP) - LRP decomposes the prediction into contributions of each input feature. This method is more complex to implement but provides detailed insights into the decision-making process.

4. Explainable AI (XAI) Libraries

a. LIME (Local Interpretable Model-agnostic Explanations) - LIME approximates the model locally with an interpretable model. You can use Python libraries like `lime` to implement this.

b. SHAP (SHapley Additive exPlanations) - SHAP values explain the output of a model by computing the contribution of each feature. This can be done using Python libraries like `shap`.

Hope this helps.

Answer 2

[Prasanna]

Hi Rabeah,

It is my understanding that you have built a CNN for signal classification and want to interpret the decision-making process of the same. The methods that you mentioned have their own way to interpret the CNN models:

• ‘imageLIME’: Good for understanding individual predictions and local explanations.
• ‘occlusionSensitivity’: Useful for identifying important regions in the input.
• ‘deepDreamImage’: Helps visualize what features the network is looking for.
• ‘gradCAM’: Effective for visualizing class-discriminative regions.
• ‘drise’: Provides robust explanations by considering a wide range of perturbations.

Each of these methods has its own strengths and can provide different insights into the model’s decision-making process. You can try a combination of these methods to get a better insight on the model. You can refer to the following documentation to learn more about feature selection for signal classification applications: https://www.mathworks.com/help/deeplearning/ug/feature-selection-based-on-deep-learning-interpretability-for-signal-classification-applications.html

Hope this helps!