Context Information Significantly Improves Brain Computer Interface Performance - a Case Study on Text Entry Using a Language Model Assisted BCI.

Abstract

We present recent results on the design of the RSVP Keyboard - a brain computer interface (BCI) for expressive language generation for functionally locked-in individuals using rapid serial visual presentation of letters or other symbols such as icons. The proposed BCI design tightly incorporates probabilistic contextual information obtained from a language model into the single or multi-trial event related potential (ERP) decision mechanism. This tight fusion of contextual information with instantaneous and independent brain activity is demonstrated to potentially improve accuracy in a dramatic manner. Specifically, a simple regularized discriminant single-trial ERP classifier's performance can be increased from a naive baseline of 75% to 98% in a 28-symbol alphabet operating at 5% false ERP detection rate. We also demonstrate results which show that trained healthy subjects can achieve real-time typing accuracies over 90% mostly relying on single-trial ERP evidence when supplemented with a rudimentary n-gram language model. Further discussion and preliminary results include our initial efforts involving a locked-in individual and our efforts to train him to improve his skill in performing the task.