A Hierarchical Detection Method for Steady State Peripheral Visual Evoked Potential

Using steady state visual evoked potential (SSVEP) brain-machine interface (BMI) system for a long time will cause strong visual fatigue. Reducing the number of redundant flicker stimuli can effectively reduce the area of visual stimuli on the screen, thus improving the user experience and reducing visual fatigue when using the SSVEP-BCIs. The steady state visual peripheral visual evoked potential (SSPVEP) paradigm removes redundant stimuli. At present, there are few signal processing and detection algorithms designed according to the characteristics of EEG signals under SSPVEP paradigm. In this paper, a hierarchical detection method is proposed to improve the performance of EEG signal detection under SSPVEP paradigm. The proposed method makes full use of the applicable characteristics of distance similarity and correlation coefficient similarity in signal detection. This method can effectively improve the detection performance of EEG signals under SSPVEP paradigm when the sampling time is set from 0.2 s to 4.6 s. The detection accuracy of this algorithm is 72.79% when the sampling time is 4.6s, while that of the previous algorithm is 71.54%.

• Publication year

  2023

• Venue

  2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

• Publication date

  2023-11-14

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361425
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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