# Context-Aware Recursive Bayesian Graph Traversal in BCIs

## TL;DR

This paper introduces probabilistic graphical models that incorporate context and EEG evidence to improve decision accuracy in noninvasive brain-computer interfaces, especially for users with poor calibration performance.

## Contribution

It proposes two novel probabilistic graphical models utilizing context and EEG data for enhanced graph-based decision-making in BCIs, demonstrating improved performance.

## Key findings

- Probabilistic models outperform traditional methods in low calibration scenarios.
- Context-aware models achieve comparable results to high calibration users.
- Simulation shows significant performance boost with the proposed approach.

## Abstract

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.