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Noninvasive brain computer interfaces (BCI), and more specificallyElectroencephalography (EEG) based systems for intent detection need tocompensate for the low signal to noise ratio of EEG signals. In manyapplications, the temporal dependency information from consecutive decisionsand contextual data can be used to provide a prior probability for the upcomingdecision. In this study we proposed two probabilistic graphical models (PGMs),using context information and previously observed EEG evidences to estimate aprobability distribution over the decision space in graph based decision-makingmechanism. In this approach, user moves a pointer to the desired vertex in thegraph in which each vertex represents an action. To select a vertex, a Selectcommand, or a proposed probabilistic Selection criterion (PSC) can be used toautomatically detect the user intended vertex. Performance of different PGMsand Selection criteria combinations are compared over a keyboard based on agraph layout. Based on the simulation results, probabilistic Selectioncriterion along with the probabilistic graphical model provides the highestperformance boost for individuals with pour calibration performance andachieving the same performance for individuals with high calibrationperformance.

Context-Aware Recursive Bayesian Graph Traversal in BCIs