Application of a reconstruction technique in detection of dominant SSVEP frequency. (February 2018)

• Record Type: Journal Article
• Authors: Wu, Zhenghua
• Is Part Of: Biomedical signal processing and control. Volume 40(2018)
• Page Start: 226
• Page End: 233
• Publication Date: 2018-02
• Subjects: Steady-state visual evoked potential (SSVEP) -- Brain-computer interface (BCI) -- Reconstruction extraction (RE) -- Power spectrum (PS) -- Canonical correlation analysis (CCA); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.09.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An EEG segment can be reconstructed into series of segments in which the SSVEP part is synchronized. The SNR of SSVEP can be improved by superposing the reconstructed EEG segments. The RE method combines the SSVEP extraction methods in the time and frequency domains. The RE method can be applied to only one signal electrode with a high detection accuracy compared to the CC method. Abstract: Steady-state visual evoked potentials (SSVEPs) have a number of specific properties such as an oscillating feature when compared to event-related potentials (ERPs). Based on this oscillating property, a short electroencephalogram (EEG) segment containing an SSVEP can be used to reconstruct a series of EEG segments where the SSVEP has the same initial phase but the phase of the background EEG varies randomly. When these reconstructed EEG segments are averaged, the background EEG is weakened, whereas the SSVEP is strengthened. Therefore, the signal-to-noise ratio (SNR) of the SSVEP in the averaged signal is significantly improved. This reconstruction technique is first proposed in this work, and the method of extracting SSVEPs based on this technique is referred to as the reconstruction extraction (RE) method. The RE, power spectrum (PS), and canonical correlation analysis (CCA) methods were applied to EEG segments that were 1s in length in order to detect SSVEPs. The results show that the detection accuracy of the RE method is similar to that of the CCA method, although it is higher than that of the PS method in situations where the SSVEP has low strength. However, in contrast to the CCA, the RE can be applied using only one signal electrode. This suggests that the RE method can be adopted in a real-time SSVEP-based brain–computer interface (BCI) system.