Multi-Channel SSVEP Pattern Recognition Based on MUSIC

Abstract:

Brain computer interfaces (BCIs) have become a research hotspot in recent years because of great potentials to help disabled people communicate with the outside world. Among different paradigms, steady state visual evoked potential (SSVEP)-based BCIs are commonly implemented in real applications, because they provide higher signal to noise ratio (SNR) and greater information transfer rate (ITR) than other BCI techniques. Various algorithms have been employed for SSVEP signal processing, like fast Fourier transform (FFT), wavelet analysis and canonical correlation analysis (CCA). In this paper, a new method based on multiple signal classification (MUSIC) was proposed for SSVEP feature extraction. The experimental results proved that it could provide higher frequency resolution and the recognition accuracy was excellent via adjusting some parameters.

• Periodical: Applied Mechanics and Materials (Volume 539)
• Pages: 84-88
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.539.84
• Online since: July 2014
• Authors: Kun Chen*, Quan Liu, Qing Song Ai
• Keywords: Brain Computer Interface (BCI), Feature Extraction, Music, SSVEP

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