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Article

A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

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Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
2
Department of Biomedical Engineering, Wichita State University, Wichita, KS 67260, USA
*
Authors to whom correspondence should be addressed.
This manuscript is a part of a doctoral dissertation of Md Rakibul Mowla.
Current address: Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32603, USA.
Brain Sci. 2020, 10(10), 734; https://doi.org/10.3390/brainsci10100734
Received: 18 September 2020 / Revised: 2 October 2020 / Accepted: 6 October 2020 / Published: 14 October 2020
(This article belongs to the Special Issue Collection on Neural Engineering)
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. View Full-Text
Keywords: brain-computer interfaces (BCI); classification methods; P300 speller; P3 latency estimation; sparse autoencoders (SAE) brain-computer interfaces (BCI); classification methods; P300 speller; P3 latency estimation; sparse autoencoders (SAE)
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MDPI and ACS Style

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. https://doi.org/10.3390/brainsci10100734

AMA Style

Mowla MR, Gonzalez-Morales JD, Rico-Martinez J, Ulichnie DA, Thompson DE. A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation . Brain Sciences. 2020; 10(10):734. https://doi.org/10.3390/brainsci10100734

Chicago/Turabian Style

Mowla, Md R., Jesus D. Gonzalez-Morales, Jacob Rico-Martinez, Daniel A. Ulichnie, and David E. Thompson 2020. "A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation " Brain Sciences 10, no. 10: 734. https://doi.org/10.3390/brainsci10100734

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