Search Results for author:
Found 5 papers, 1 papers with code
Why is the prediction wrong? Towards underfitting case explanation via meta-classification
In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier.
Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features
The performance gap is reduced with bigger datasets, but considering the increased computational load, end-to-end training may not be profitable for this application.
Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance
In this study, we investigated the impact of long-term recordings on motor imagery decoding from two main perspectives: model requirements regarding dataset size and potential for patient adaptation.
Decoding ECoG signal into 3D hand translation using deep learning
These models have a limited representational capacity and may fail to capture the relationship between ECoG signal and continuous hand movements.
An exact counterfactual-example-based approach to tree-ensemble models interpretability
And the black-boxes approaches, which are used to explain such model decisions, suffer from a lack of accuracy in tracing back the exact cause of a model decision regarding a given input.
