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Updated: Jul 10, 2026

EEG Mu Rhythm in Typical and Atypical Development
11:50

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Published on: April 9, 2014

An embedded EEG analyzing system based on muC/os-II.

Boqiang Liu1, Yanyan Zhang, Zhongguo Liu

  • 1School of Control Science and Engineering, Shandong University, Jinan, 250061, China.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary

This paper presents an EEG analysis system using ARM and muC/os-II. The system efficiently synchronizes event and EEG signals, demonstrating feasible filter behaviors for practical applications.

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Area of Science:

  • Biomedical Engineering
  • Embedded Systems
  • Signal Processing

Background:

  • Electroencephalography (EEG) signal analysis is crucial for diagnosing neurological conditions.
  • Real-time processing of EEG data requires efficient embedded systems.
  • Existing systems may face challenges in synchronization and data handling.

Purpose of the Study:

  • To design and implement an EEG analyzing system using Advanced RISC Machines (ARM) and the muC/os-II real-time operating system.
  • To detail the system design, including event signal generation, synchronization, data acquisition, preprocessing, and transmission.
  • To present the development of a high-capability amplifier and embedded subsystem software.

Main Methods:

  • System design based on ARM architecture and muC/os-II real-time operating system.
  • Implementation of multi-tasking capabilities within the muC/os-II environment.
  • Development of data acquisition, preprocessing, and USB data transmission protocols.
  • Design of a high-capability amplifier and communication protocols between PC and equipment.

Main Results:

  • Successful synchronization between event signals and EEG signals.
  • Detailed system configurations for data acquisition, preprocessing, and USB transmission.
  • Feasible filter behaviors demonstrated in the final equipment tests.
  • Effective multi-tasking system realized in muC/os-II.

Conclusions:

  • The developed EEG analyzing system is feasible and effective.
  • The system provides a robust platform for real-time EEG signal processing.
  • The integration of ARM and muC/os-II offers a capable solution for embedded EEG analysis.