Towards a POMDP-based Control in Hybrid Brain-Computer Interfaces

Brain-Computer Interfaces (BCI) provide a unique communication channel between the brain and computer systems. After extensive research and implementation on ample fields of application, numerous challenges to assure reliable and quick data processing have resulted in the hybrid BCI (hBCI) paradigm, consisting on the combination of two BCI systems. However, not all challenges have been properly addressed (e.g. re-calibration, idle-state modelling, adaptive thresholds, etc) to allow hBCI implementation outside of the lab. In this paper, we review electroencephalography based hBCI studies and state potential limitations. We propose a sequential decision-making framework based on Partially Observable Markov Decision Process (POMDP) to design and to control hBCI systems. The POMDP framework is an excellent candidate to deal with the limitations raised above. To illustrate our opinion, an example of architecture using a POMDP-based hBCI control system is provided, and future directions are discussed. We believe this framework will encourage research efforts to provide relevant means to combine information from BCI systems and push BCI out of the laboratory.