Synchrosqueezing with short-time fourier transform method for trinary frequency shift keying encoded SSVEP

Area of Science:

• Neuroscience
• Signal Processing
• Biomedical Engineering

Background:

• Steady state visual evoked potential (SSVEP) based brain-computer interfaces (BCI) face limitations due to restricted frequencies.
• Trinary frequency shift keying (TFSK) modulated visual stimuli offer a potential solution to expand BCI instruction sets.
• Developing reliable recognition algorithms is crucial for TFSK-modulated SSVEP-BCI systems.

Purpose of the Study:

• To develop and validate a robust recognition algorithm for SSVEP-BCI systems utilizing TFSK modulated stimuli.
• To enhance the accuracy and efficiency of BCI instruction recognition in SSVEP paradigms.

Main Methods:

• Simulated TFSK signals and electroencephalography (EEG) data were analyzed.
• Signal reconstruction and characteristic frequency extraction were performed using empirical mode decomposition (EMD), singular value decomposition (SVD), and synchrosqueezing with short-time Fourier transform (SST).
• Canonical correlation analysis (CCA) and coherent demodulation were employed for BCI instruction recognition.

Main Results:

• SST demonstrated superior performance in extracting characteristic frequencies from simulated signals.
• The combined approach of SST and coherent demodulation achieved higher accuracy and information translation rates for EEG signal recognition.
• This method proved more effective than other tested algorithms in the context of TFSK-modulated SSVEP-BCI.

Conclusions:

• The proposed method combining SST and coherent demodulation is effective for SSVEP systems using TFSK modulated stimuli.
• This approach offers a promising advancement for improving the performance and usability of SSVEP-based BCIs.
• The findings support the application of this advanced signal processing technique in BCI development.