Performance Model of Selection Techniques for P300-Based Brain-Computer Interfaces (BCI)

Jean-Baptiste Sauvan, et. al., INRIA/IRISA

Summary

A model based on Markov theory has been proposed to predict performance of selection techniques of a P300 based BCI.

Details

Typical BCIs have been based on positive 300ms EEG signals. On being shown a display with one of the objects flashing, user starts to count which results in P300 being detected 300ms later. The interaction technique has been represented as static Markov chains. This allow authors to compute time required to perform an action and corresponding number of flashes needed. Three different techniques were proposed and validated against model:

• Global : where any object can flash alternatively (& hence directly selected).
• N-chotomic: where user selects one of N sub regions before selecting single target within that sub-region.
• Relative: where user ‘move’ his selection from currently targeted object by moving to its neighbours.

 

Review

While I did not understand the mathematics behind Markov theory but it was interesting to read on BCI.

Disclaimer

The work discussed above is an original work presented at CHI 2009 by the authors/affiliations indicated at the starting of this post. This post in itself was created as part of course requirement of CPSC 436.