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A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency

Chengcheng Hua1, Hong Wang1, Hong Wang2

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Summary
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A new method, Brain Connection based on Stacked Autoencoder (BCSAE), efficiently computes functional brain networks (FBNs) from EEG data. This approach accurately detects operator proficiency in mineral grinding, outperforming existing methods.

Keywords:
EEGFunctional brain connectionproficiency detectionstacked autoencodersupervised fine-tuning

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Functional Brain Network (FBN) analysis is crucial for understanding cortical interactions.
  • Current methods for computing FBNs are time-consuming and require extensive searching.
  • Operator proficiency assessment in industrial processes like mineral grinding is essential.

Purpose of the Study:

  • To develop a novel, semi-data-driven method for computing FBNs to assess operator proficiency.
  • To reduce the time researchers spend searching for optimal FBN computation methods.
  • To evaluate the effectiveness of the proposed method in detecting operator skill levels during mineral grinding.

Main Methods:

  • A semi-supervised, multi-layered stacked autoencoder (SAE) was used to encode multi-channel EEG data.
  • Functional brain connections were built by computing dissimilarity between encoded electrode data.
  • The proposed Brain Connection based on Stacked Autoencoder (BCSAE) method was applied to EEG data from mineral grinding operators.

Main Results:

  • The BCSAE method generated more separable features with reduced redundancy compared to control methods.
  • Classification accuracy for operator proficiency reached 96.18% using BCSAE.
  • BCSAE significantly outperformed Phase Locking Value (PLV) at 92.19% and Phase Lag Index (PLI) at 78.39%.

Conclusions:

  • The BCSAE method offers an efficient and effective approach for computing FBNs.
  • This novel method accurately assesses operator proficiency in real-time industrial tasks.
  • BCSAE provides a valuable tool for improving operator training and process control.