title: Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine.
abstract: BACKGROUND
  Brain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. However, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject's physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual's difference in SSVEP is needed but was seldom reported.
  
  
  METHODS
  This paper presents a multiclass support vector machine (SVM)-based classification approach for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user's gaze targets.
  
  
  RESULTS
  The on-line performance using the proposed approach has achieved high accuracy (89.88 ± 4.76%), fast responding time (effective epoch length = 1.13 ± 0.02 s), and the information transfer rate (ITR) was 50.91 ± 8.70 bits/min.
  
  
  CONCLUSIONS
  The multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject's SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets.
authors.affiliation: Department of Electrical Engineering, National Central University, Jhongli, Taiwan.
authors.forename: Chia-Lung
authors.initials: CL
authors.lastname: Yeh
authors.forename: Po-Lei
authors.initials: PL
authors.lastname: Lee
authors.forename: Wei-Ming
authors.initials: WM
authors.lastname: Chen
authors.forename: Chun-Yen
authors.initials: CY
authors.lastname: Chang
authors.forename: Yu-Te
authors.initials: YT
authors.lastname: Wu
authors.forename: Gong-Yau
authors.initials: GY
authors.lastname: Lan
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D004867:Equipment Design; D005074:Evoked Potentials, Visual; D005260:Female; D005403:Fixation, Ocular / Q000502:physiology; D006801:Humans; D008297:Male; D017711:Nonlinear Dynamics; D060388:Support Vector Machine; D055815:Young Adult