Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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 language | English |
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Title of host publication | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
Number of pages | 4 |
Publication date | 31 Oct 2013 |
Pages | 4279-4282 |
Article number | 6610491 |
ISBN (Print) | 9781457702167 |
DOIs | |
Publication status | Published - 31 Oct 2013 |
- Autocorrelation, Brain-Computer Interface, Naïve-Bayes Classifier, Steady-State Visual Evoked Potentials, Tri-training
Research areas
ID: 120786973