TY - JOUR
T1 - Integrating language models into classifiers for BCI communication
T2 - A review
AU - Speier, W.
AU - Arnold, C.
AU - Pouratian, N.
N1 - Funding Information:
This work was supported by the National Institute of Biomedical Imaging and Bioengineering Award Number K23EB014326 (NP) and the UCLA Scholars in Translational Medicine Program (NP)
Publisher Copyright:
© 2016 IOP Publishing Ltd.
PY - 2016/5/6
Y1 - 2016/5/6
N2 - Objective. The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. Approach. The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. Main results. Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. Significance. Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.
AB - Objective. The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. Approach. The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. Main results. Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. Significance. Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.
KW - P300 speller
KW - braincomputer interface
KW - language model
KW - natural language processing
KW - predictive spelling
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U2 - 10.1088/1741-2560/13/3/031002
DO - 10.1088/1741-2560/13/3/031002
M3 - Review article
C2 - 27153565
AN - SCOPUS:84973470242
SN - 1741-2560
VL - 13
JO - Journal of neural engineering
JF - Journal of neural engineering
IS - 3
M1 - 031002
ER -