Electroencephalogram training phase reduction for ubiquitous robotic brain symbiosis

Abstract

Electroencephalograms are brain-computer interfaces that consist of a series of conductors placed on the scalp, using machine-learning techniques, the P300 signal can be classified and used to command ubiquitous robotic systems. For both able-bodied and disabled subjects, the collection of training data can be an exhaustive exercise. It is the goal of this work-in-progress to substitute an extended training phase with a more generalized approach involving electroencephalogram data from multiple subjects, in an attempt to eliminate classification redundancy.