Extraction of high-frequency SSVEP for BCI control using iterative filtering based empirical mode decomposition

Chuan Chih Hsu, Chia Lung Yeh, Wai Keung Lee, Hao Teng Hsu, Kuo Kai Shyu, Lieber Po Hung Li, Tien Yu Wu, Po Lei Lee

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Steady-state visual evoked potential (SSVEP) has been regarded as an efficient way to design a brain computer interface (BCI). Most SSVEP-based BCIs utilize visual stimuli with flashing frequencies lower than 30 Hz, owing to their better signal-to-noise ratio (SNR). However, the practical applications of low-frequency SSVEP-based BCI are limited, because low-frequency SSVEP usually incur uncomfortable visual experience and the risk of photosensitive epilepsy. In contrast, SSVEP-based BCIs using higher stimulation frequencies (>40 Hz) can induce flicker fusion effect for better visualization. In this study, we studied the feasibility of using iterative filtering - empirical mode decomposition (IF-EMD) to implement a BCI cursor system. EEG signals were recorded from dry EEG electrodes with impedance matching circuits. Three stimulation frequencies, designed at 47, 50, and 53 Hz, were chosen to induce high-frequency SSVEPs, in order to control the leftward, forward and rightward movements of the BCI cursor. Ten subjects were recruited, and each subject was requested to complete a control experiment and an application experiment. In the control experiment, subjects were requested to gaze at each flickering target for thirty seconds. In the application experiment, subjects were instructed to move a cursor to reach three targets on a PC screen. The mean accuracy (Acc), command transfer interval (CTI), and information transfer rate (ITR) in the control experiment were 90.7 ± 2.9%, 1.14 ± 0.07 s, and 54.94 ± 5.41 bits/min, respectively. In the application experiment, the mean execution time and CTI were 30.0 ± 4.69 s and 1.50 ± 0.31 s, respectively.

Original languageEnglish
Article number102022
JournalBiomedical Signal Processing and Control
StatePublished - Aug 2020


  • Brain computer interface
  • Empirical mode decomposition
  • Iterative filtering
  • Steady-state visual evoked potential


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