A comparison of classification techniques to predict brain-computer interfaces accuracy using classifier-based latency estimation

Loading...
Thumbnail Image
Issue Date
2020-10-14
Authors
Mowla, Md Rakibul
Gonzalez Morales, Jesus D.
Rico Martinez, Jacob
Ulichnie, Daniel A.
Thompson, David E.
Advisor
Citation

Mowla, M.R.; Gonzalez-Morales, J.D.; Rico-Martinez, J.; Ulichnie, D.A.; Thompson, D.E. A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation . Brain Sci. 2020, 10, 734

Abstract

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p < 0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

Table of Content
Description
© 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
publication.page.dc.relation.uri