Journal
BRAIN SCIENCES
Volume 10, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/brainsci10100734
Keywords
brain-computer interfaces (BCI); classification methods; P300 speller; P3 latency estimation; sparse autoencoders (SAE)
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Funding
- National Science Foundation [1910526]
- National Institutes of Health [5R25GM19968]
- Developing Scholars Program (DSP) at Kansas State University
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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 (p0.001) 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.
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