Probabilistic simulation framework for EEG-based BCI design (Orhan et al., 2016).

Image from OrhanOrhan, U., Nezamfar, H., Akcakaya, M., Erdogmus, D., Higger, M., Moghadamfalahi, M., Fowler, A., Roark, B., Oken B. & Fried-Oken, M. (2016). Probabilistic simulation framework for EEG-based BCI design. Brain-Computer Interfaces, 3, 171-185.

doi: 10.1080/2326263X.2016.1252621

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.

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