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A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network.

Yaqi Chu1,2,3, Xingang Zhao1,2, Yijun Zou1,2,3

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.

Frontiers in Neuroscience
|October 17, 2018
PubMed
Summary

This study introduces a new method for decoding incomplete electroencephalogram (EEG) signals in brain-computer interfaces (BCI). The Lomb-Scargle periodogram (LSP) and deep belief network (DBN) improve motor imagery recognition despite data loss.

Keywords:
brain-computer interfacedecoding schemedeep belief networkincomplete motor imagery EEGpower spectral density

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Accurate electroencephalogram (EEG) decoding for motor imagery brain-computer interfaces (BCI) is challenging, especially with signal artifacts and data loss.
  • Conventional methods reject contaminated EEG segments, leading to decoding interruptions and reduced BCI usability.
  • Developing robust decoding strategies for incomplete EEG signals is crucial for practical BCI applications.

Purpose of the Study:

  • To propose and evaluate a novel decoding scheme for incomplete motor imagery EEG signals.
  • To address the challenge of extreme artifacts and data loss in EEG data for BCI.
  • To enhance the stability and continuity of BCI output during online and long-term applications.

Main Methods:

  • A new decoding scheme combining Lomb-Scargle periodogram (LSP) for feature extraction and deep belief network (DBN) for classification was developed.
  • Two data removal techniques were employed to handle EEG segments with extreme artifacts and data loss, avoiding complete segment rejection.
  • Power spectral density (PSD) features were extracted using LSP from the remaining EEG portions.
  • A DBN classifier, optimized from a restricted Boltzmann machine (RBM) structure, was used for recognizing incomplete motor imagery EEG.

Main Results:

  • The Lomb-Scargle periodogram (LSP) demonstrated robust estimation of power spectral density (PSD) features from incomplete EEG data.
  • Comparative experiments showed the proposed LSP-DBN scheme significantly improved decoding performance for incomplete motor imagery EEG compared to conventional methods.
  • The DBN classifier outperformed the support vector machine (SVM) in recognizing incomplete motor imagery EEG signals.

Conclusions:

  • The proposed LSP-DBN decoding scheme offers a viable alternative for processing motor imagery EEG contaminated by artifacts and data loss.
  • This approach enhances the stability, smoothness, and continuity of BCI output, making it suitable for online and long-term use.
  • The findings contribute to the development of more reliable and practical brain-computer interface systems.