TY - JOUR
T1 - Probabilistic simulation framework for EEG-based BCI design
AU - Orhan, Umut
AU - Nezamfar, Hooman
AU - Akcakaya, Murat
AU - Erdogmus, Deniz
AU - Higger, Matt
AU - Moghadamfalahi, Mohammad
AU - Fowler, Andrew
AU - Roark, Brian
AU - Oken, Barry
AU - Fried-Oken, Melanie
N1 - Funding Information:
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.
Publisher Copyright:
© 2016, © 2016 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - 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.
AB - 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.
KW - Electroencephalography
KW - event-related potentials
KW - simulation
KW - steady-state visually evoked potentials
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U2 - 10.1080/2326263X.2016.1252621
DO - 10.1080/2326263X.2016.1252621
M3 - Article
AN - SCOPUS:85046128725
SN - 2326-263X
VL - 3
SP - 171
EP - 185
JO - Brain-Computer Interfaces
JF - Brain-Computer Interfaces
IS - 4
ER -