TY - JOUR
T1 - A comparison of stimulus types in online classification of the P300 Speller using language models
AU - Speier, William
AU - Deshpande, Aniket
AU - Cui, Lucy
AU - Chandravadia, Nand
AU - Roberts, Dustin
AU - Pouratian, Nader
N1 - Publisher Copyright:
© 2017 Speier et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/4
Y1 - 2017/4
N2 - The P300 Speller is a common brain-computer interface communication system. There are many parallel lines of research underway to overcome the system's low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary. The goal of this study is therefore twofold. First, we aim to compare the famous faces stimulus paradigm with an existing alternative stimulus paradigm currently used in commercial systems (i.e., character inversion). Second, we test these methods with language model integration to assess whether different optimization approaches can be combined to further improve BCI communication. In offline analysis using a previously published particle filter method, famous faces stimuli yielded superior results to both standard and inverting stimuli. In online trials using the particle filter method, all 10 subjects achieved a higher selection rate when using the famous faces flashing paradigm than when using inverting flashes. The improvements achieved by these methods are therefore complementary and a combination yields superior results to either method implemented individually when tested in healthy subjects.
AB - The P300 Speller is a common brain-computer interface communication system. There are many parallel lines of research underway to overcome the system's low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary. The goal of this study is therefore twofold. First, we aim to compare the famous faces stimulus paradigm with an existing alternative stimulus paradigm currently used in commercial systems (i.e., character inversion). Second, we test these methods with language model integration to assess whether different optimization approaches can be combined to further improve BCI communication. In offline analysis using a previously published particle filter method, famous faces stimuli yielded superior results to both standard and inverting stimuli. In online trials using the particle filter method, all 10 subjects achieved a higher selection rate when using the famous faces flashing paradigm than when using inverting flashes. The improvements achieved by these methods are therefore complementary and a combination yields superior results to either method implemented individually when tested in healthy subjects.
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U2 - 10.1371/journal.pone.0175382
DO - 10.1371/journal.pone.0175382
M3 - Article
C2 - 28406932
AN - SCOPUS:85017556578
SN - 1932-6203
VL - 12
JO - PloS one
JF - PloS one
IS - 4
M1 - e0175382
ER -