Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.
Rifai ChaiGanesh R NaikSai Ho LingHung T NguyenPublished in: Biomedical engineering online (2017)
Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.
Keyphrases
- deep learning
- machine learning
- neural network
- end stage renal disease
- resting state
- mental health
- ejection fraction
- working memory
- newly diagnosed
- chronic kidney disease
- loop mediated isothermal amplification
- peritoneal dialysis
- functional connectivity
- prognostic factors
- optical coherence tomography
- healthcare
- physical activity
- multiple sclerosis
- health information
- social media