On the Need for On-Line Learning in Brain-Computer Interfaces

In this paper we motivate the need for on-line learning in BCI and illustrate its benefits with the simplest method, namely fixed learning rates. However, the use of this method is supported by the risk of hampering the user to acquire suitable control of the BCI if the embedded classifier changes too rapidly. We report the results with 3 beginner subjects in a series of consecutive recording, where the classifiers are iteratively trained with the data of a given session and tested on the next session. At the end of these sessions 2 of the subjects reach a suitable performance that is close to allow them to start operating a brain-actuated device.