An Interactive Control Strategy is More Robust to Non-Optimal Classification Boundaries

摘要

We consider a new paradigm for EEG-based brain computer interface (BCT) cursor control involving signaling satisfaction or dissatisfaction with the current motion direction instead of the usual direct control of signaling rightward or leftward desired motion. We start by assuming that the same underlying EEC signals are used to either signal directly the intent for right and left motion or to signal satisfaction and dissatisfaction with the current motion. We model the paradigm as an absorbing Markov chain and show that while both the standard system and the new interactive system have equal information transfer rate (ITR) when the Bayes optimal classification boundary (between the underlying EEG feature distributions used for the two classes) is exactly known and non-changing, the interactive system is much more robust to using a suboptimal classification boundary. Due to non-stationarity of EEG recordings, in real systems the classification boundary will often be suboptimal for the current EEG signals. We note that a variable step size gives a higher ITR for both systems (but the same robustness improvement of the interactive system remains). Finally, we present a way to probabilistically combine classifiers of natural signals of satisfaction and dissatisfaction with classifiers using standard left/right controls.