title: Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers.
abstract: Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed. Hence incorporation of a language model based prior or additional evidence is vital to improve accuracy and speed. In this paper, we study the effects of Bayesian fusion of an n-gram language model with a regularized discriminant analysis ERP detector for EEG-based BCIs. The letter classification accuracies are rigorously evaluated for varying language model orders as well as number of ERP-inducing trials. The results demonstrate that the language models contribute significantly to letter classification accuracy. Specifically, we find that a BCI-speller supported by a 4-gram language model may achieve the same performance using 3-trial ERP classification for the initial letters of the words and using single trial ERP classification for the subsequent ones. Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the word rate of a BCI based typing system.
authors.affiliation: Cognitive Systems Laboratory, Northeastern University, Boston, MA, USA.
authors.forename: Umut
authors.initials: U
authors.lastname: Orhan
authors.forename: Deniz
authors.initials: D
authors.lastname: Erdogmus
authors.forename: Brian
authors.initials: B
authors.lastname: Roark
authors.forename: Shalini
authors.initials: S
authors.lastname: Purwar
authors.forename: Kenneth E
authors.initials: KE
authors.lastname: Hild
authors.forename: Barry
authors.initials: B
authors.lastname: Oken
authors.forename: Hooman
authors.initials: H
authors.lastname: Nezamfar
authors.forename: Melanie
authors.initials: M
authors.lastname: Fried-Oken
mesh_terms: D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007802:Language; D008962:Models, Theoretical; D009323:Natural Language Processing; D013647:Task Performance and Analysis; D014584:User-Computer Interface; D014956:Writing