Improved classification in tactile bcis using a noisy label model.

In this study, we model the tactile ERP generation as label noise and develop a novel BCI paradigm for binary communication designed to minimize label confusion. The classification model is based on a modified Gaussian mixture and trained using expectation maximization (EM). Finally, we show after testing on multiple subjects that this approach yields cross-validated accuracies for all users which are significantly above chance and suggests that such an approach is robust and reliable for a variety of binary communication-based applications.
PMID: 31110601 [PubMed]

Source: Proceedings - International Symposium on Biomedical Imaging - May 23, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research