An Automatic SSVEP Component Selection Measure for High-Performance Brain-Computer Interface
This paper proposed an automatic steady-state visual evoked potential (SSVEP) component selection (SCS) measure for a high-performance SSVEP-based brain-computer interface (SBCI) system. First, multi-electrode raw electroencephalogram signals are spatially pre-processed using a blind source separation technique resulting in multi-source components. The SCS measure of each component is then calculated by continuous wavelet transform (CWT), and the ensemble features that contain the weighted CWT energy of individual SSVEP harmonic are extracted. Second, the SSVEP component with maximal SCS measure is considered to have the highest signal-to-noise ratio. In our SBCI system, six stimulus frequencies served as the input patterns. Offline analyses were performed, through which the common electrode locations, the time window size, and the number of harmonics were defined. Thereafter the results of our method were compared with those of others. We next carried out an online test of the SBCI for 11 subjects using eight common electrode locations, a 1.5-s time window, and the first and second harmonics. The test results showed that our method achieved an average accuracy of 95.2 % and a practical bit rate of 68.2 bits/min.
KeywordsBrain-computer interface (BCI) Electroencephalogram (EEG) Steady-state visual evoked potential (SSVEP) Automatic SSVEP component selection measure
This work was supported in part by the National Science Foundation of China (NSFC) under Grant Nos. 90820305 and 60775040. The authors would like to thank all subjects for their participation.
- 7.Luo A, Sullivan TJ (2010) A user-friendly SSVEP-based brain-computer interface using a time-domain classifier. J Neural Eng 7(2)Google Scholar
- 9.Zhang ZM, Li XQ, Deng ZD (2010) A CWT-based SSVEP classification method for brain-computer interface system. In: Proceedings of the IEEE international conference on intelligent control and information processing. Dalian, pp 43–48Google Scholar
- 16.Bin G, Gao X, Yan Z, Hong B, Gao S (2009) An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J Neural Eng 6(4):046002Google Scholar
- 18.Yan Z, Gao X, Bin G, Hong B, Gao S (2009) A half-field stimulation pattern for SSVEP-based brain-computer interface. Proc IEEE Eng Med Biol Soc 2009(2006):6461–6464Google Scholar
- 20.Regan D (1989) Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, New YorkGoogle Scholar
- 21.Cichocki A, Amari S (2002) Adaptive blind signal and image processing: learning algorithms and applications (ch. 4). Wiley, ChichesterGoogle Scholar
- 27.Sanei S, Chambers JA (2007) EEG signal processing (ch. 2). Wiley, ChichesterGoogle Scholar