title: Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.
abstract: OBJECTIVE
  Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established.
  
  
  METHODS
  This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects.
  
  
  RESULTS
  The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ∼33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min(-1).
  
  
  CONCLUSIONS
  By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, People's Republic of China.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Chen
authors.forename: Yijun
authors.initials: Y
authors.lastname: Wang
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
mesh_terms: D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D003143:Communication Devices for People with Disabilities; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*; D015443:Word Processing / Q000379:methods