Publications
2013
Jin, Jing; Sellers, Eric W.; Zhang, Yu; Daly, Ian; Wang, Xingyu; Cichocki, Andrzej
Whether generic model works for rapid ERP-based BCI calibration Journal Article
In: Journal of Neuroscience Methods, vol. 212, no. 1, pp. 94-99, 2013.
Abstract | Links | BibTeX | Tags: BCI, Classification, Event-related potentials, Generic model
@article{Jin2012,
title = {Whether generic model works for rapid ERP-based BCI calibration},
author = {Jing Jin and Eric W. Sellers and Yu Zhang and Ian Daly and Xingyu Wang and Andrzej Cichocki},
doi = {10.1016/j.jneumeth.2012.09.020},
year = {2013},
date = {2013-01-13},
journal = {Journal of Neuroscience Methods},
volume = {212},
number = {1},
pages = {94-99},
abstract = {Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would be helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system.},
keywords = {BCI, Classification, Event-related potentials, Generic model},
pubstate = {published},
tppubtype = {article}
}
Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would be helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system.