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

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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Bibliographic Details
Main Authors: Md Rakibul Mowla, Jesus D. Gonzalez-Morales, Jacob Rico-Martinez, Daniel A. Ulichnie, David E. Thompson
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Brain Sciences
Subjects:
brain-computer interfaces (BCI)
classification methods
P300 speller
P3 latency estimation
sparse autoencoders (SAE)
Online Access:https://www.mdpi.com/2076-3425/10/10/734

Internet

https://www.mdpi.com/2076-3425/10/10/734

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