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This paper focuses on discrete wavelet-based filter decomposition approach to identify the presence of steady-state visual evoked potential (SSVEP) signal frequency from the single-channel recorded electroencephalogram (EEG) signal. SSVEPs are periodic signals that occur into visual cortex region of brain when a subject’s gaze his/her attention on visual stimuli flickering at specific frequencies. Accurate identification of SSVEP Frequency from recorded EEG signal is still challenging task due to presence of artifacts and unwanted EEG signal components. In this study, the author proposed the combined approach of discrete wavelet-based filter bank approach and power spectrum peak analysis (PSPA) using welch method to detect the SSVEP frequency from the single channel recorded EEG signal. The performance is measured in terms of detection accuracy of each flickering frequency of subject-1 to subject-4. The obtained result indicates that the hybrid approach of DWT plus PSPA outperformed as compared PSPA approach. The average detection accuracy of all four subjects is 86.70 percentage using DWT plus PSPA approach percentage while average detection accuracy obtained by PSPA approach alone for all the four subjects are 79.70 percentage.

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Quantifying Target Frequency Using Combined Approach of DWT and Power Spectrum Peak Analysis for SSVEP BCI System

  • 会议论文

  • 发表日期：

    2026年

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  • Mukesh Kumar Ojha,
  • Sindhu Hak Gupta,
  • Sourabh Choudhary,
  • Rahul Singh,
  • Sourabh Saha

摘要

This paper focuses on discrete wavelet-based filter decomposition approach to identify the presence of steady-state visual evoked potential (SSVEP) signal frequency from the single-channel recorded electroencephalogram (EEG) signal. SSVEPs are periodic signals that occur into visual cortex region of brain when a subject’s gaze his/her attention on visual stimuli flickering at specific frequencies. Accurate identification of SSVEP Frequency from recorded EEG signal is still challenging task due to presence of artifacts and unwanted EEG signal components. In this study, the author proposed the combined approach of discrete wavelet-based filter bank approach and power spectrum peak analysis (PSPA) using welch method to detect the SSVEP frequency from the single channel recorded EEG signal. The performance is measured in terms of detection accuracy of each flickering frequency of subject-1 to subject-4. The obtained result indicates that the hybrid approach of DWT plus PSPA outperformed as compared PSPA approach. The average detection accuracy of all four subjects is 86.70 percentage using DWT plus PSPA approach percentage while average detection accuracy obtained by PSPA approach alone for all the four subjects are 79.70 percentage.