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A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain-computer

Hanjui Chang1,2, Yue Sun3,4, Shuzhou Lu3,4

  • 1Department of Mechanical Engineering, College of Engineering, Shantou University, Shantou, 515063, China. changhj@stu.edu.cn.

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Summary

Optimizing injection molding parameters for brain-computer interfaces (BCI) significantly reduces node displacement. This advancement enhances the reliability of noninvasive BCIs by minimizing damage to integrated electronic components.

Keywords:
Brain–computer interface (BCI)Entropy valueIn-mold electronic decoration (IME)Latin hypercube sampling (LHS)Multistrategy differential evolution (MSDE) algorithmNode displacement

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Area of Science:

  • Manufacturing Engineering
  • Biomedical Engineering
  • Materials Science

Background:

  • Injection molding is a key plastic processing technique for creating shaped parts.
  • In-mold electronics (IME) integrate circuit components directly into molded parts.
  • Brain-computer interfaces (BCIs) offer direct neural control of external devices, with noninvasive methods using EEG signals.

Purpose of the Study:

  • To optimize injection molding parameters for noninvasive brain-computer interface (BCI) helmet production.
  • To minimize node displacement and residual stress in molded parts containing in-mold electronics (IME).
  • To enhance the reliability and performance of BCIs by ensuring optimal integration of electronic components.

Main Methods:

  • Designed a helmet model and a printed circuit film for EEG signal reception.
  • Utilized an in-mold electronics (IME) injection mold with conductive ink for component integration.
  • Employed a multistrategy differential evolutionary algorithm and Latin hypercubic sampling to optimize injection molding parameters.

Main Results:

  • Optimized injection molding parameters reduced node displacement from 0.585 mm to 0.027 mm, achieving a 95.38% optimization rate.
  • The study established relationships between injection molding parameters and target values for optimal outcomes.
  • Evaluated BCI reliability by checking output voltage differences post-optimization.

Conclusions:

  • Optimized injection molding parameters are crucial for reliable noninvasive BCI production.
  • Minimizing node displacement through parameter optimization significantly improves the performance of integrated electronic circuits.
  • This research demonstrates a viable method for enhancing BCI technology through advanced manufacturing processes.