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Found 5 papers, 1 papers with code
Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding
In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision.
A Strong and Simple Deep Learning Baseline for BCI MI Decoding
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
BCI Motor Imagery datasets usually are small and have different electrodes setups.
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals.
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies
In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies.
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