Online Classifier Adaptation in High Frequency EEG

In recent years a number of non-invasive Brain-Computer Interfaces have been developed that determine the intent of a subject by analysing the Electroencephalograph(EEG) signals up to frequencies of 40Hz. The use of high frequency EEG features have recently been proposed as alternative or additional features in EEG-based BCIs. In this paper we examine the performance of several feature bands, and evaluate the performance on online classifier adaptation on these features. Our analysis shows that the higher frequency band perform very well under online classifier adaptation for all the frequency bands, particularly for the higher bands.