title: Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information.
abstract: OBJECTIVE
  A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) using joint frequency-phase coding.
  
  
  METHODS
  The key idea is to transfer SSVEP templates from the existing subjects to a new subject to enhance the detection of SSVEPs. Under this framework, transfer template-based canonical correlation analysis (tt-CCA) methods were developed for single-channel and multi-channel conditions respectively. In addition, an online transfer template-based CCA (ott-CCA) method was proposed to update EEG templates by online adaptation.
  
  
  RESULTS
  The efficiency of the proposed framework was proved with a simulated BCI experiment. Compared with the standard CCA method, tt-CCA obtained an 18.78% increase of accuracy with a data length of 1.5 s. A simulated test of ott-CCA further received an accuracy increase of 2.99%.
  
  
  CONCLUSIONS
  The proposed simple yet efficient framework significantly facilitates the use of SSVEP BCIs using joint frequency-phase coding. This study also sheds light on the benefits from exploring and exploiting inter-subject information to the electroencephalogram (EEG)-based BCIs.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, People's Republic of China.
authors.forename: Peng
authors.initials: P
authors.lastname: Yuan
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Chen
authors.forename: Yijun
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*