Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters pres...

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Bibliographic Details
Main Authors: Bender, Thomas, Kjaer, Troels W., Thomsen, Carsten E., Sorensen, Helge B. D., Puthusserypady, S.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Autocorrelation
Brain-Computer Interface
Brain-computer interfaces
Correlation
Error analysis
Naïve-Bayes Classifier
Signal to noise ratio
Steady-State Visual Evoked Potentials
Training
Tri-training
Visualization
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Description
Summary:This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters presented on a 100 Hz CRT-monitor, three scalp electrodes for signal acquisition, a gUSB-amp for preamplification and two PCs for data-processing and stimulus control respectively. Preliminary test results of the system on nine healthy subjects, with and without tri-training, indicates that the accuracy improves as a result of tri-training.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2013.6610491