Probabilistic simulation framework for EEG-based BCI design

Abstract

A simulation framework could decrease the burden of attending long and tiring experimental sessions on the potential users of brain-computer interface (BCI) systems. Specifically during the initial design of a BCI, a simulation framework that could replicate the operational performance of the system would be a useful tool for designers to make design choices. In this manuscript, we develop a Monte Carlo-based probabilistic simulation framework for electroencephalography (EEG) based BCI design. We employ one event-related potential (ERP) based typing and one steady-state evoked potential (SSVEP) based control interface as testbeds. We compare the results of simulations with real-time experiments. Even though over- and underestimation of the performance is possible, the statistical results over the Monte Carlo simulations show that the developed framework generally provides a good approximation of the real-time system performance.

Keywords:

• Electroencephalography
• event-related potentials
• steady-state visually evoked potentials
• simulation

Acknowledgements

This research is supported by NSF (CNS-1136027, IIS-1149570), NIH (2R01DC009834) and NIDRR (H133E140026). The authors acknowledge help and contributions from collaborators in the OHSU Reknew Projects Group and CSL at Northeastern.