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Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface.

Shuai Wang1, Zhiguo Luo2, Shaokai Zhao2

  • 1School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300380, China.

Bioengineering (Basel, Switzerland)
|January 22, 2024
PubMed
Summary

This study introduces a Sparrow Search Algorithm-optimized Deep Belief Network (SSA-DBN) for improved motor imagery (MI) brain-computer interface (BCI) classification. The novel method significantly enhances accuracy in recognizing brain signals for BCI applications.

Keywords:
brain-computer interfacedeep belief networkempirical mode decompositionmotor imagerysparrow search algorithm

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCI) face challenges in accurately recognizing motor imagery (MI) brain signals.
  • Existing classification methods for MI require improvement in accuracy and robustness.

Purpose of the Study:

  • To develop an advanced classification method for MI-BCI systems.
  • To enhance the accuracy and robustness of MI signal recognition using an optimized deep learning model.

Main Methods:

  • Empirical Mode Decomposition (EMD) was used to extract EEG features.
  • A Deep Belief Network (DBN) was optimized using the Sparrow Search Algorithm (SSA), creating the SSA-DBN model.
  • The SSA-DBN model's performance was evaluated on two public and one private dataset.

Main Results:

  • The SSA-DBN method demonstrated superior classification accuracy compared to baseline methods.
  • On a private dataset, SSA-DBN achieved 87.83% accuracy, a 10.38% improvement over standard DBN.
  • Significant accuracy improvements were also observed on the BCI IV 2a (86.14%) and SMR-BCI (87.21%) datasets.

Conclusions:

  • The SSA-DBN model offers enhanced classification capabilities for MI-BCI.
  • This approach shows potential for advancing the field of brain-computer interfaces through improved signal recognition.