Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Enhancing P300-BCI performance using latency estimation

Enhancing P300-BCI performance using latency estimation AbstractBrain Computer Interfaces (BCIs) offer restoration of communication to those with the most severe movement impairments, but performance is not yet ideal. Previous work has demonstrated that latency jitter, the variation in timing of the brain responses, plays a critical role in determining BCI performance. In this study, we used Classifier-Based Latency Estimation (CBLE) and a wavelet transform to provide information about latency jitter to a second-level classifier. Three second-level classifiers were tested: least squares (LS), step-wise linear discriminant analysis (SWLDA), and support vector machine (SVM). Of these three, LS and SWLDA performed better than the original online classifier. The resulting combination demonstrated improved detection of brain responses for many participants, resulting in better BCI performance. Interestingly, the performance gain was greatest for those individuals for whom the BCI did not work well online, indicating that this method may be most suitable for improving performance of otherwise marginal participants. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Enhancing P300-BCI performance using latency estimation

Enhancing P300-BCI performance using latency estimation

Abstract

AbstractBrain Computer Interfaces (BCIs) offer restoration of communication to those with the most severe movement impairments, but performance is not yet ideal. Previous work has demonstrated that latency jitter, the variation in timing of the brain responses, plays a critical role in determining BCI performance. In this study, we used Classifier-Based Latency Estimation (CBLE) and a wavelet transform to provide information about latency jitter to a second-level classifier. Three...
Loading next page...
 
/lp/taylor-francis/enhancing-p300-bci-performance-using-latency-estimation-Gd3G07EL83
Publisher
Taylor & Francis
Copyright
© 2017 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2017.1338010
Publisher site
See Article on Publisher Site

Abstract

AbstractBrain Computer Interfaces (BCIs) offer restoration of communication to those with the most severe movement impairments, but performance is not yet ideal. Previous work has demonstrated that latency jitter, the variation in timing of the brain responses, plays a critical role in determining BCI performance. In this study, we used Classifier-Based Latency Estimation (CBLE) and a wavelet transform to provide information about latency jitter to a second-level classifier. Three second-level classifiers were tested: least squares (LS), step-wise linear discriminant analysis (SWLDA), and support vector machine (SVM). Of these three, LS and SWLDA performed better than the original online classifier. The resulting combination demonstrated improved detection of brain responses for many participants, resulting in better BCI performance. Interestingly, the performance gain was greatest for those individuals for whom the BCI did not work well online, indicating that this method may be most suitable for improving performance of otherwise marginal participants.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Jul 3, 2017

Keywords: Brain-Computer Interfaces (BCIs); P300 Speller; Classifier Based Latency Estimation (CBLE)

References