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Found 4 papers, 1 papers with code
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.
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
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.
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