NDLI: Context information significantly improves brain computer interface performance - a case study on text entry using a language model assisted BCI

Content Provider	IEEE Xplore Digital Library
Author	Orhan, U. Erdogmus, D. Hild, K.E. Roark, B. Oken, B. Fried-Oken, M.
Copyright Year	2011
Description	Author affiliation: Oregon Health and Science University, Portland, USA (Hild, K.E.; Roark, B.; Oken, B.; Fried-Oken, M.) \|\| Cognitive Systems Laboratory, Northeastern University, Boston, MA, USA (Orhan, U.; Erdogmus, D.)
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.
Starting Page	132
Ending Page	136
File Size	137127
Page Count	5
File Format	PDF
ISBN	9781467303217
ISSN	10586393
e-ISBN	9781467303231
DOI	10.1109/ACSSC.2011.6189970
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Electroencephalography Context Real time systems Context modeling Visualization Keyboards
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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