Lin Chin-Teng, Wang Yuhling, Chen Sheng-Fu, Huang Kuan-Chih, Liao Lun-De
Human-centric AI Centre (HAI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
Australia Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
Med Biol Eng Comput. 2023 Nov;61(11):3003-3019. doi: 10.1007/s11517-023-02879-y. Epub 2023 Aug 11.
Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.
脑机接口（BCI）实现了大脑与外部世界之间的通信。此类技术已得到广泛研究。然而，具有高信号质量的BCI仪器通常体积庞大且笨重。因此，记录脑电图（EEG）信号是一项不便的任务。近年来，片上系统（SoC）方法已被集成到BCI研究中，并且已开发出用于无线便携式设备的传感器；然而，仍有大量工作要做。随着神经科学研究的进展，EEG信号分析需要更准确的数据。由于蓝牙无线传输技术的带宽有限，必须使用具有16个以上通道的EEG测量系统来降低采样率并防止数据丢失。因此，本研究的目标是开发一种通过Wi-Fi传输信号的多通道、高分辨率（24位）、高采样率的EEG BCI设备。我们相信该系统可用于神经科学研究。本工作中提出的EEG采集系统基于带有Wi-Fi子系统的Cortex-M4微控制器，提供多通道设计并改善了信号质量。该系统与湿传感器、Ag/AgCl电极和干传感器兼容。基于LabVIEW的用户界面通过Wi-Fi传输接收EEG数据，并保存原始数据以供离线分析。在先前的认知实验中，事件标签已使用推荐标准232（RS-232）进行通信。所开发的系统通过事件相关电位（ERP）和稳态视觉诱发电位（SSVEP）实验进行了验证。我们的实验结果表明，该系统适用于记录EEG测量数据，并且在实际应用中具有潜力。所开发系统的优点包括其高采样率和高放大倍数。此外，未来可将物联网（IoT）技术集成到该系统中，以进行远程实时分析或边缘计算。
