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Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations

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  • 1Department of Mechanical Engineering, College of Engineering, Shantou University, Shantou 515063, China.

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Summary

This study optimizes injection molding for brain-computer interfaces by using Kriging and sparse regression models. The new method significantly reduces electrode displacement, improving EEG signal accuracy and device performance.

Keywords:
KrigingPDEsbrain–computer interface (BCI)in-mold electronics (IME)node displacement

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Area of Science:

  • Manufacturing Engineering
  • Biomedical Engineering
  • Materials Science

Background:

  • Injection molding is a key manufacturing process for plastic products.
  • In-mold electronics (IME) integrates electronic components into molded parts.
  • Brain-computer interfaces (BCIs) require precise integration of components like electrode arrays for signal transmission.

Purpose of the Study:

  • To optimize injection molding parameters for embedding electrode lines in thin films for BCIs.
  • To improve the stability and accuracy of electroencephalography (EEG) signal transmission.
  • To reduce the cost and enhance the integration of microelectrode arrays.

Main Methods:

  • Utilized a Kriging prediction model combined with sparse regression partial differential equations (PDEs).
  • Focused on optimizing key injection molding parameters: holding pressure, holding time, and melting temperature.
  • Evaluated the displacement of nodes in the film to ensure stability and reliability.

Main Results:

  • Achieved optimal injection parameters: 525 MPa holding pressure, 50 s holding time, and 285 °C melting temperature.
  • Reduced average node displacement of the Utah array (UA) from 0.19 mm to 0.89 µm.
  • Demonstrated a 95.32% optimization rate in node displacement, enhancing signal transmission accuracy.

Conclusions:

  • The proposed method effectively optimizes injection molding for IME applications in BCIs.
  • The optimized parameters ensure the stability and reliability of electrode lines during manufacturing.
  • This advancement improves EEG signal accuracy and overall BCI system performance.