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Found 3 papers, 1 papers with code
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface
Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential.
Predicting feature imputability in the absence of ground truth
Data imputation is the most popular method of dealing with missing values, but in most real life applications, large missing data can occur and it is difficult or impossible to evaluate whether data has been imputed accurately (lack of ground truth).
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.
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