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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Number of pages4
Publication date31 Oct 2013
Pages4279-4282
Article number6610491
ISBN (Print)9781457702167
DOIs
Publication statusPublished - 31 Oct 2013

    Research areas

  • Autocorrelation, Brain-Computer Interface, Naïve-Bayes Classifier, Steady-State Visual Evoked Potentials, Tri-training

ID: 120786973