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Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Author

Listed:
  • Vladimir A. Maksimenko
  • Semen A. Kurkin
  • Elena N. Pitsik
  • Vyacheslav Yu. Musatov
  • Anastasia E. Runnova
  • Tatyana Yu. Efremova
  • Alexander E. Hramov
  • Alexander N. Pisarchik

Abstract

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high-frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

Suggested Citation

  • Vladimir A. Maksimenko & Semen A. Kurkin & Elena N. Pitsik & Vyacheslav Yu. Musatov & Anastasia E. Runnova & Tatyana Yu. Efremova & Alexander E. Hramov & Alexander N. Pisarchik, 2018. "Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity," Complexity, Hindawi, vol. 2018, pages 1-10, August.
  • Handle: RePEc:hin:complx:9385947
    DOI: 10.1155/2018/9385947
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    Cited by:

    1. Andreev, Andrey V. & Ivanchenko, Mikhail V. & Pisarchik, Alexander N. & Hramov, Alexander E., 2020. "Stimulus classification using chimera-like states in a spiking neural network," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Mengxin Liu & Wenyuan Tao & Xiao Zhang & Yi Chen & Jie Li & Chung-Ming Own, 2019. "GO Loss: A Gaussian Distribution-Based Orthogonal Decomposition Loss for Classification," Complexity, Hindawi, vol. 2019, pages 1-10, December.

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