A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation
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 line...
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doaj-9f47bf74613d4712987b4ec4705358902020-11-25T03:55:07ZengMDPI AGBrain Sciences2076-34252020-10-011073473410.3390/brainsci10100734A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency EstimationMd Rakibul Mowla0Jesus D. Gonzalez-Morales1Jacob Rico-Martinez2Daniel A. Ulichnie3David E. Thompson4Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USADepartment of Biomedical Engineering, Wichita State University, Wichita, KS 67260, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAP300-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 (<inline-formula><math display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.001</mn></mrow></semantics></math></inline-formula>) 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.https://www.mdpi.com/2076-3425/10/10/734brain-computer interfaces (BCI)classification methodsP300 spellerP3 latency estimationsparse autoencoders (SAE) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Md Rakibul Mowla Jesus D. Gonzalez-Morales Jacob Rico-Martinez Daniel A. Ulichnie David E. Thompson |
spellingShingle |
Md Rakibul Mowla Jesus D. Gonzalez-Morales Jacob Rico-Martinez Daniel A. Ulichnie David E. Thompson A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation Brain Sciences brain-computer interfaces (BCI) classification methods P300 speller P3 latency estimation sparse autoencoders (SAE) |
author_facet |
Md Rakibul Mowla Jesus D. Gonzalez-Morales Jacob Rico-Martinez Daniel A. Ulichnie David E. Thompson |
author_sort |
Md Rakibul Mowla |
title |
A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation |
title_short |
A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation |
title_full |
A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation |
title_fullStr |
A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation |
title_full_unstemmed |
A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation |
title_sort |
comparison of classification techniques to predict brain-computer interfaces accuracy using classifier-based latency estimation |
publisher |
MDPI AG |
series |
Brain Sciences |
issn |
2076-3425 |
publishDate |
2020-10-01 |
description |
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 (<inline-formula><math display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.001</mn></mrow></semantics></math></inline-formula>) 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. |
topic |
brain-computer interfaces (BCI) classification methods P300 speller P3 latency estimation sparse autoencoders (SAE) |
url |
https://www.mdpi.com/2076-3425/10/10/734 |
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