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Research on dry electrode SSVEP classification algorithm based on improved convolutional neural network

Author

Listed:
  • Mengjia He
  • Yingnian Wu
  • Tao Wang
  • Yujie Chen

Abstract

SSVEP signal is mainly collected by a wet electrode. Wet electrode collection steps are tedious, and dry electrode collection is simple and easy to apply, so the study of dry electrode is the focus of SSVEP research. Although the dry electrode is easy to collect, the ITR is low and the signal intensity of the EEG signal of the subjects is highly differentiated. At present, the cross trial classification of SSVEP is the focus and difficulty of research. In order to be able to meet the situation without calibration and can still have higher adaptability, in this paper, deep convolutional neural network is improved for the classification of dry electrode SSVEP. The classification of ten types of SSVEP dry electrode signals can reach the classification accuracy of 91.11%, and the ITR of 1S can reach 54.87, which has a very broad application scenario.

Suggested Citation

  • Mengjia He & Yingnian Wu & Tao Wang & Yujie Chen, 2021. "Research on dry electrode SSVEP classification algorithm based on improved convolutional neural network," International Journal of Service and Computing Oriented Manufacturing, Inderscience Enterprises Ltd, vol. 4(1), pages 70-88.
    • Handle: RePEc:ids:ijscom:v:4:y:2021:i:1:p:70-88
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    Cited by:

    1. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    2. Mohammed Sabri & Rosanna Verde & Antonio Balzanella & Fabrizio Maturo & Hamid Tairi & Ali Yahyaouy & Jamal Riffi, 2024. "A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 264-288, July.

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