Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs.

ABSTRACT: For Brain-Computer interfaces (BCIs), system calibration is a lengthy but necessary process for successful operation. Co-adaptive BCIs aim to shorten training and imply positive motivation to users by presenting feedback already at early stages: After just 5 min of gathering calibration data, the systems are able to provide feedback and engage users in a mutual learning process. In this work, we investigate whether the retraining stage of co-adaptive BCIs can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. The aim of the current work was to evaluate whether a semi-supervised co-adaptive BCI could successfully compete with a supervised co-adaptive BCI model. In a supporting two-cla

SUBMITTER: Schwarz A 

PROVIDER: S-EPMC6828633 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature