title: An online hybrid BCI system based on SSVEP and EMG.
abstract: OBJECTIVE
  A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance.
  
  
  METHODS
  A 60-target hybrid BCI speller was built in this study. A single trial was divided into two stages: a stimulation stage and an output selection stage. In the stimulation stage, SSVEP and EMG were used together. Every stimulus flickered at its given frequency to elicit SSVEP. All of the stimuli were divided equally into four sections with the same frequency set. The frequency of each stimulus in a section was different. SSVEPs were used to discriminate targets in the same section. Different sections were classified using EMG signals from the forearm. Subjects were asked to make different number of fists according to the target section. Canonical Correlation Analysis (CCA) and mean filtering was used to classify SSVEP and EMG separately. In the output selection stage, the top two optimal choices were given. The first choice with the highest probability of an accurate classification was the default output of the system. Subjects were required to make a fist to select the second choice only if the second choice was correct.
  
  
  RESULTS
  The online results obtained from ten subjects showed that the mean accurate classification rate and ITR were 81.0% and 83.6 bits min(-1) respectively only using the first choice selection. The ITR of the hybrid system was significantly higher than the ITR of any of the two single modalities (EMG: 30.7 bits min(-1), SSVEP: 60.2 bits min(-1)). After the addition of the second choice selection and the correction task, the accurate classification rate and ITR was enhanced to 85.8% and 90.9 bit min(-1).
  
  
  CONCLUSIONS
  These results suggest that the hybrid system proposed here is suitable for practical use.
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China.
authors.forename: Ke
authors.initials: K
authors.lastname: Lin
authors.forename: Andrea
authors.initials: A
authors.lastname: Cinetto
authors.forename: Yijun
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Chen
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D002755:Choice Behavior / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009862:Online Systems; D010775:Photic Stimulation / Q000379:methods*; D011897:Random Allocation; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult