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Motor Imagery EEG Classification Based on CEEMDAN-CWT Characterization

Published: 22 May 2024 Publication History

Abstract

Brain-computer interface (BCI) is a technology that uses EEG signals to realize human-computer interaction. Motor imagery is a commonly used EEG paradigm, which has the advantage of active control and can be used in neurorehabilitation, prosthetic control and other fields. Based on the BCIC IV 2b binary classification dataset, this paper studies the EEG preprocessing and time-frequency representation algorithms. We designed the CEEMDAN-CWT method to reduce the complexity of the algorithm by reducing the number of channels, using sample entropy and the K-means clustering method. By comparing various time-frequency representation methods, the proposed method exhibits a good ability to improve feature separability. The experimental results showed that the optimized CEEMDAN-CWT method improves the MI-EEG classification accuracy and stability. It could reach a higher accuracy and a lower standard deviation on the 9 subjects compared with the CWT time-frequency representation method.

References

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K. LaFleur, K. Cassady, A. Doud, K. Shades, E. Rogin, and B. He, “Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface,” Journal of Neural Engineering, Vol. 10, No. 4, pp. 046003, Jun. 2013.
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W. -Y. Hsu and Y. -W. Cheng, "EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 31, pp. 1659-1669, 2023.
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S. K. Bashar, A. R. Hassan and M. I. H. Bhuiyan, "Motor imagery movements classification using multivariate EMD and short time Fourier transform," 2015 Annual IEEE India Conference (INDICON), IEEE, New Delhi, India, 2015, pp. 1-6.
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H. Peng, "Single-Trial Classification of fNIRS Signals in Four Directions Motor Imagery Tasks Measured From Prefrontal Cortex," in IEEE Transactions on NanoBioscience, vol. 17, no. 3, pp. 181-190, July 2018.
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S. Zhang, Y. Ma, Q. Zhang and Y. Gao, "Motor imagery electroencephalogram de-noising method based on EEMD and improved wavelet threshold," 2018 Chinese Control And Decision Conference (CCDC), IEEE, Shenyang, China, 2018, pp. 5589-5594.
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L. Qin and B. He, “A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications,” Journal of Neural Engineering, vol. 2, no. 4, pp. 65–72, Aug. 2005.
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G. Liu, T. Lan, and W. Zhou, “Multiscale time-frequency method for multiclass Motor Imagery Brain Computer Interface,” Computers in Biology and Medicine, vol. 143, pp. 105299–105299, Apr. 2022.
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VSIP '23: Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing
November 2023
237 pages
ISBN:9798400709272
DOI:10.1145/3638682
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 May 2024

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Author Tags

  1. CEEMDAN
  2. CWT
  3. EEG classification
  4. Motor imagery
  5. Shallow CNN

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