A novel paradigm for multiple target selection using a two class brain computer interface

Abstract

A brain computer interface (BCI) allows a person to communicate with external devices using electroencephalogram (EEG) or other brain signals. A typical BCI scheme consists of data acquisition, feature extraction and classification. Using the classifier output, a control command is issued to the intended devices and the subject is provided appropriate feedback. As a part of feedback, a graphical user interface (GUI) plays a very important role as a front-end display for the BCI user and enhancing overall communication bandwidth. This paper focuses on the interface design aspect of a BCI so as to provide effective control of a wheelchair or robot arm application. A motor imagery prediction based paradigm is used to create a semi synchronous interface with a focus on presentation of a new task for selection as well as to optimally utilize the subject intentions. From a theoretical assessment, it is expected that the overall time required to select from six choices using the proposed GUI will be much less compared to existing designs. Also, being a two class paradigm, it is expected that the probability of error occurrence is minimized along with a quicker traverse between choices and this may allow a limited bandwidth BCI to operate an external device with multiple degrees of freedom and choose from multiple different choices efficiently and effectively.