title: Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.
abstract: BACKGROUND
  One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals.
  
  
  METHODS
  This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia.
  
  
  RESULTS
  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.
authors.affiliation: Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia. Rifai.Chai@uts.edu.au.
authors.forename: Rifai
authors.identifier: http://orcid.org/0000-0002-1922-7024
authors.initials: R
authors.lastname: Chai
authors.affiliation: Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia.
authors.forename: Ganesh R
authors.initials: GR
authors.lastname: Naik
authors.affiliation: Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia.
authors.forename: Sai Ho
authors.initials: SH
authors.lastname: Ling
authors.affiliation: Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia.
authors.forename: Hung T
authors.initials: HT
authors.lastname: Nguyen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D035843:Biomedical Research / Q000295:instrumentation*; D001921:Brain / Q000502:physiology / Q000503:physiopathology; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000295:instrumentation*; D055724:Electrophysiological Phenomena; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D013119:Spinal Cord Injuries / Q000503:physiopathology; D059015:Wireless Technology
keywords: Artificial neural network; Brain–computer interface; Cyber physical system; Electroencephalography; Embedded system; Hybrid system