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Found 5 papers, 1 papers with code
Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance
This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG).
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space
We propose a design space for XAI4BCI, considering the evolving need to visualize and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle.
SymNet: Symmetrical Filters in Convolutional Neural Networks
The main hypothesis of this paper is that the symmetrical constraint reduces the number of free parameters in the network, and it is able to achieve near identical performance to the modern methodology of training.
Rotation Invariant Descriptors for Galaxy Morphological Classification
In this paper, we propose to evaluate the performance of different families of descriptors for the classification of galaxy morphologies.
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface
Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes.
