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 under estimation 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: Brain computer interfaces; electroencephalography; event related potentials; simulation; steady state visually evoked potentials.

Figure 1

Graphical Model (Epoch k)

Figure 2

Screen-shots of the system during a) Calibration and b) Mastery Task.

Figure 3

Mazes used during the 4 Mastery tasks.

Figure 4

Estimated and actual performance measures: (a) accuracy, and (b) number of trials per target.

Figure 5

Grand averages of ERPs for the P1 and P5 electrodes.

Figure 6

Successful completion probability for each subject for each scenario for simulation and experiment. Right and left panels correspond to simulation and experimental results, respectively.

Figure 7

Duration to copy 8 phrases for each subject for each scenario for simulation and experiment. This corresponds to the total duration spent on attempting to type all of the phrases. Right and left panels correspond to simulation and experimental results, respectively.