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<p>In biomedical applications, the data acquisition device for brain activities has gained significant popularity. Numerous brain-acquisition devices have been implemented using various electronic devices. While some devices are sophisticated, the majority of commonly used devices are expensive. In this paper, we design and implement a soft intellectual property (IP) core device using an FPGA (Field Programmable Gate Array) applied to brain acquisition data and analysis. This FPGA-based device is used for band identification of the electroencephalography (EEG) signal and functional near-infrared spectroscopy (fNIRS). It is also built around it and eliminates artifacts from the patient's original EEG and fNIRS signals. Autoregressive moving averages (ARMA) are used in EEG and fNIRS signal analyses to find the features of different signal bands. The developed prototype of a soft IP core device possesses desirable electrical characteristics, such as a power consumption of 0.082 W, a lower operating voltage of 2.5 V and current of 0.018 A, and a lower core junction temperature of 25&#xa0;°C, which is better than many preexisting prototypes. This soft IP-core device is built for brain and bioengineering applications.</p>

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Identification of Frequency Band of EEG and fNIRS Signals Based on FPGA

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Faijah Rashid,
  • Sheikh Md. Rabiul Islam

摘要

In biomedical applications, the data acquisition device for brain activities has gained significant popularity. Numerous brain-acquisition devices have been implemented using various electronic devices. While some devices are sophisticated, the majority of commonly used devices are expensive. In this paper, we design and implement a soft intellectual property (IP) core device using an FPGA (Field Programmable Gate Array) applied to brain acquisition data and analysis. This FPGA-based device is used for band identification of the electroencephalography (EEG) signal and functional near-infrared spectroscopy (fNIRS). It is also built around it and eliminates artifacts from the patient's original EEG and fNIRS signals. Autoregressive moving averages (ARMA) are used in EEG and fNIRS signal analyses to find the features of different signal bands. The developed prototype of a soft IP core device possesses desirable electrical characteristics, such as a power consumption of 0.082 W, a lower operating voltage of 2.5 V and current of 0.018 A, and a lower core junction temperature of 25 °C, which is better than many preexisting prototypes. This soft IP-core device is built for brain and bioengineering applications.