comojin1994/proximity-to-boundary-score
A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization through Proximity-to-Boundary Scoring
This project helps researchers and scientists working with Electroencephalography (EEG) data to improve the accuracy and reliability of their machine learning models. It takes raw EEG datasets, which often contain noisy samples, and processes them to reduce the influence of these problematic data points. The outcome is a more generalized and robust model that performs better on new, unseen EEG data. This is intended for neuroscience researchers, sleep study analysts, or anyone developing applications based on interpreting brain signals.
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Use this if your deep learning models trained on EEG data struggle with low generalization ability due to noisy samples in your datasets.
Not ideal if you are working with non-EEG biomedical signals or if you require extensive hyperparameter optimization for noise detection, as this tool aims to minimize that need.
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Dec 05, 2024
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