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Arxiv
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Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
- Brain-computer interfaces (BCIs) can benefit from uncertainty quantification to enhance accuracy in Motor Imagery classification tasks.
- Research compares uncertainty quantification abilities of established BCI classifiers like CSP-LDA and MDRM against Deep Learning methods.
- CSP-LDA and MDRM-T provide the best uncertainty estimates, while Deep Ensembles and CNNs excel in classifications for Motor Imagery BCI tasks.
- Models showcase the ability to differentiate between easy and difficult classifications, enabling improved accuracy by rejecting ambiguous samples.
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