A case study of linear classifiers adapted using imperfect labels derived from human event-related potentials

作者：Zeyl Timothy J; Chau Tom^*

来源：Pattern Recognition Letters, 2014, 37: 54-62.

DOI：10.1016/j.patrec.2013.05.020

• Partially supervised learning
• Imperfect labels
• Adaptive classification
• EEG
• Binary classification
• Event-related potentials

Electroencephalogram signals used to control brain-computer interfaces (BCIs) are nonstationary, a problem that makes classification of mental tasks difficult in real-time. Event-related potentials associated with BCI errors have the potential to be used as online labels for adaptation of BCI classifiers; however, detection of event-related potentials is imperfect, which makes this a partially supervised classification problem. In this study, two linear binary classifiers are adapted using uncertain labels on artificial data sets representing various scenarios of concept drift as well as on a real motor imagery BCI data set. Both perfectly and imperfectly simulated labels are incorporated into the classifiers which are adapted in the following two ways: (i) only after trials where BCI mistakes were detected and (ii) after every trial regardless of whether or not an error was detected. We find that all data sets benefit from adaptation using imperfect labels and that adapting after all trials results in better performance than adapting only after detected errors, especially when the labels are imperfect and the classes are inseparable.

• 出版日期2014-2-1