Attributed Feature Graphs (AFGs) represent CAD features as attributed nodes and relations as directed edges to enable GNN surrogate models that predict design performance with feature-level interpretability on the CarHoods10K dataset.
Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain- computer interface
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A modular EEG-based BCI with S4D deep learning classifier achieves 84% offline accuracy and enables real-time control for a tetraplegic user, with 73% success in post-competition validation.
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Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon
A modular EEG-based BCI with S4D deep learning classifier achieves 84% offline accuracy and enables real-time control for a tetraplegic user, with 73% success in post-competition validation.