Comparing Classification Methods for Asynchronous Brain-computer Interfaces

Abstract

In the field of Brain-Computer Interfacing, the asynchronous approach adds a new ”no movement” class to the classic trial-based synchronous approach. This ”no movement” class is very unbalanced (most of the data will belong to this class) and therefore causes a major classification obstacle. This study investigates the suitability of different classification methods for use in an asynchronous Brain-Computer Interface. We used a Guitar Hero like game to gather EEG data to compare the classification methods using the Readiness Potential in different movement conditions. Based on the results, we recommend to use a hierarchical method that first classifies between movement and no movement, and only if movement is detected, classifies between left and right movement.