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Found 4 papers, 1 papers with code
Latent Alignment with Deep Set EEG Decoders
We delineate a trade-off relationship between increased classification accuracy when alignment is performed at later modeling stages, and susceptibility to class-imbalance in the set of trials that the statistics are computed on.
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.
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
The second task required to transfer models trained on the subjects of one or more source motor imagery datasets to perform inference on two target datasets, providing a small set of personalized calibration data for multiple test subjects.
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions.
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