Bayesian learning from multi-way EEG feedback for robot navigation and target identification.

ABSTRACT: Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously while users merely observe assistive robots performing tasks. Using reactive brain signals, existing studies have addressed robot navigation tasks with a very limited number of potential target locations. Moreover, they use only binary, error-vs-correct classification of robot actions, leaving more detailed information unutilised. In this study a virtual robot had to navigate towards, and identify, target locations in both small and large grids, wherein any location could be the target. For the first time, we apply a system utilising detailed

SUBMITTER: Wirth C 

PROVIDER: S-EPMC10560278 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature