Context-Aware Recursive Bayesian Graph Traversal in BCIs
This work addresses intent detection for individuals using noninvasive BCIs, but it is incremental as it builds on existing probabilistic methods with specific enhancements.
The study tackled the problem of low signal-to-noise ratio in EEG-based brain-computer interfaces by proposing probabilistic graphical models and a probabilistic selection criterion to improve intent detection in graph-based decision-making, achieving the highest performance boost for individuals with poor calibration performance while maintaining performance for those with high calibration.
Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism. In this approach, user moves a pointer to the desired vertex in the graph in which each vertex represents an action. To select a vertex, a Select command, or a proposed probabilistic Selection criterion (PSC) can be used to automatically detect the user intended vertex. Performance of different PGMs and Selection criteria combinations are compared over a keyboard based on a graph layout. Based on the simulation results, probabilistic Selection criterion along with the probabilistic graphical model provides the highest performance boost for individuals with pour calibration performance and achieving the same performance for individuals with high calibration performance.