A MUSIC-based method for SSVEP signal processing

Australas Phys Eng Sci Med. 2016 Mar;39(1):71-84. doi: 10.1007/s13246-015-0398-6. Epub 2016 Jan 29.

Abstract

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

Keywords: Brain computer interface (BCI); Feature extraction; Multiple signal classification (MUSIC); Steady state visual evoked potential (SSVEP).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Amplifiers, Electronic
  • Computer Simulation
  • Evoked Potentials, Visual / physiology*
  • Humans
  • Photic Stimulation
  • Signal Processing, Computer-Assisted*
  • Statistics as Topic
  • Time Factors