1 Department of Electrical Engineering, Technical University of Denmark2 Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark3 Technical University of Denmark4 Copenhagen University Hospital5 Copenhagen Center for Health Technology, Center, Technical University of Denmark
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.
Ieee Engineering in Medicine and Biology Society Conference Proceedings, 2013, Vol 2013, p. 4279-4282