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Enhancing DNN Explainability in BCI

This project tackled the challenge posed by the opaque nature of Deep Neural Network (DNN) models, especially crucial in safety-driven fields like Brain-Computer Interface (BCI) applications. The primary aim was to unleash the full potential of DNN models, specifically in analyzing EEG signals.

In this initial study, I highlighted the importance of not just relying on quantitative performance but also considering qualitative evaluation. The investigation delved into a key paper in the field, providing a comprehensive analysis as a starting point. I then compared the model against common alternatives, tweaking various hyper-parameters to validate its performance.

To demystify the "black-box," I utilized the Layer-wise Relevance Propagation (LRP) method, revealing insightful details about each model's reliability. LRP proved invaluable in explaining the inner workings of the DNN, making it a useful tool in interpreting complex neural network decisions and enhancing transparency.


Stay curious with me.

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