SignBrain: 无线可穿戴脑电采集技术

孟庆桐; 常东明; 曹姗; 胡若晨; 蒋田仔; 左年明

doi:10.16383/j.aas.c240578

SignBrain: 无线可穿戴脑电采集技术

doi: 10.16383/j.aas.c240578 cstr: 32138.14.j.aas.c240578

1.
中国科学院自动化研究所北京 100190

详细信息

作者简介:
孟庆桐：中国科学院自动化研究所脑网络组研究中心工程师. 主要研究方向为脑信号检测和神经调控硬件系统研发.E-mail: qingtong.meng@ia.ac.cn

常东明：中国科学院自动化研究所脑网络组研究中心工程师. 主要研究方向脑信号检测, 软件研发和脑电分析算法设计.Email: dongming.chang@ia.ac.cn

曹姗：中国科学院自动化研究所脑网络组研究中心工程师. 主要研究方向为嵌入式系统, 脑电设备研发. E-mail: shan.cao@ia.ac.cn

胡若晨：中国科学院自动化研究所助理研究员. 主要研究方向为视觉脑-机接口和脑电信号编解码算法. E-mail: ruochen.hu@ia.ac.cn

蒋田仔：中国科学院自动化研究所脑网络组研究中心教授. 主要研究方向脑影像分析、脑网络组、数字孪生脑临床应用. E-mail: jiangtz@nlpr.ia.ac.cn

左年明：中国科学院自动化研究所脑网络组研究中心教授. 主要研究方向可穿戴脑机接口应用. 本文通信作者.E-mail: nmzuo@nlpr.ia.ac.cn

计量
- 文章访问数: 9
- HTML全文浏览量: 22
- 被引次数: 0
出版历程
- 网络出版日期: 2025-03-26

SignBrain: Wireless Wearable EEG Device

1.
Institute of Automation Chinese Academy of Sciences, Beijing 100190

More Information

Author Bio:
MENG Qing-Tong　Engineer at the Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. His research interest covers brain signals probing and neuromodulation hardware development

CHANG Dong-Ming　Engineer at the Brainnetome Center , Institute of Automation, Chinese Academy of Sciences. His research interest covers brain signals probing, software development and algorithms design for EEG analysis

CAO SHAN　Engineer at the Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. Her research interest covers embedded systems and EEG device development

HU Ruo-Chen　Assistant Professor at the Brainnetome Center , Institute of Automation, Chinese Academy of Sciences. His research interest covers visual brain-computer interface and EEG encoding and decoding algorithm

JIANG Tian-Zi　Professor at the Brainnetome Center , Institute of Automation, Chinese Academy of Sciences. His research interest Brain imaging analysis, brainetome, digital twin brain and clinical applications

ZUO Nian-Ming　Professor at the Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. His research interest wearable bidirectional brain-computer interface and its applications

摘要

摘要: 介绍一种自主研发的无线可穿戴非侵入式脑电信号采集技术: SignBrain (型号P). SignBrain设备为爪形结构, 设计符合国际10-20导联标准, 具有18个盐水电极, 配合万向活动抱紧部件, 始终保持电极与头皮紧密接触, 弥补了头型较大、发量较多佩戴使用的问题. 设备不用打导电膏实现了“即戴即用”的使用方式, 采集的脑电信号通过低功耗蓝牙实时传输至软件系统, 系统支持在线阻抗检测、Marker同步记录等功能. 同时研发了与设备配套的PC端软件、应用接口以及移动终端 (手机、平板电脑等) 软件, 能在线、离线、远程查看数据. SignBrain技术已在临床医院及相关单位完成了小批量的试用, 通过脑机交互领域中闭眼想象写字实验、高频视觉诱发实验来验证设备的可靠性及稳定性. 关于设备的开发和应用讨论请访问网站: www.SignBrain.cn.
- SignBrain /
- 脑电 /
- 可穿戴 /
- 非侵入 /
- 脑机接口
Abstract: This manuscript introduces a wireless wearable non-invasive EEG device: SignBrain (model P). The SignBrain device has a claw-shaped structure and is designed to meet the international 10-20 electrode layout standard. It has 18 saline electrodes (conduct gel is not required) and is equipped with a universal shaft structure to keep the electrodes in close contact with the scalp all the times, making up for the problem of a larger head or more hair. The collected EEG signals are transmitted to the software system in real time via low-power Bluetooth. The system supports online impedance detection and marker synchronous recording. Furthermore, we developed PC software, API and mobile terminal (mobile phones, tablets, etc.) software to match the device, which can view retrospective data online, offline and remotely. SignBrain devices have been tested in batches in hospitals. The reliability and stability of the device have been verified through closed-eye imagination writing experiments and broad frequency steady state visually evoked potential (SSVEP) experiments in the field of brain-computer interaction. For detailed discussion on the development and application of the device, please visit the homepage: www.SignBrain.cn.
- SignBrain /
- Electroencephalogram (EEG) /
- Wearable /
- Non-invasive /
- Brain-Computer Interface (BCI)

HTML全文

图 1 SignBrain设备性能检测报告

Fig. 1 SignBrain device performance test report

下载: 全尺寸图片幻灯片

图 2 设备主干结构

Fig. 2 Device backbone structure

下载: 全尺寸图片幻灯片

图 3 16通道SignBrain设备导联位置排布图

Fig. 3 Layout of the electrodes for 16-channel SignBrain device

下载: 全尺寸图片幻灯片

图 4 设备爪形结构

Fig. 4 Claw structure of the device

下载: 全尺寸图片幻灯片

图 5 万向轴抱紧件

Fig. 5 Universal shaft

下载: 全尺寸图片幻灯片

图 6 短中长三种海绵规格展示

Fig. 6 Three types of sponge: short, medium and long

下载: 全尺寸图片幻灯片

图 7 SignBrain设备效果图

Fig. 7 SignBrain device effect picture

下载: 全尺寸图片幻灯片

图 8 信号流程图

Fig. 8 Signal flow illustration

下载: 全尺寸图片幻灯片

图 9 SignBrain与Emotiv的频谱图对比

Fig. 9 Spectrogram comparison between SignBrain and Emotiv

下载: 全尺寸图片幻灯片

图 10 SignBrain与BP的频谱图对比

Fig. 10 Spectrogram comparison between SignBrain and BP

下载: 全尺寸图片幻灯片

图 11 实际应用测试图

Fig. 11 Practical application test

下载: 全尺寸图片幻灯片

图 12 5名男性被试

Fig. 12 5 male participants

下载: 全尺寸图片幻灯片

图 13 5名女性被试

Fig. 13 5 female participants

下载: 全尺寸图片幻灯片

图 14 不同性别各通道阻抗均值比较

Fig. 14 Comparison of the average impedance of each channel for different genders

下载: 全尺寸图片幻灯片

图 15 想象写字范式图

Fig. 15 Experiment for imaginary writing recognition

下载: 全尺寸图片幻灯片

图 16 26字母分类测试结果

Fig. 16 26 letter recognition results

下载: 全尺寸图片幻灯片

图 17 被试使用设备想象字母

Fig. 17 Subjects used the device to imagine letters writing

下载: 全尺寸图片幻灯片

图 18 采用BP设备与SignBrain设备想象写字实验模型训练结果

Fig. 18 Training results of the imaginary writing recognition using BP device and SignBrain device

下载: 全尺寸图片幻灯片

图 19 SignBrain设备6个频率点视觉诱发O1电极记录脑电信号FFT图

Fig. 19 Demonstrations for the six frequency evoked signals by SSVEP using SignBrain Device

下载: 全尺寸图片幻灯片

表 1 SignBrain与其他厂商便携脑电设备的技术指标对比

Table 1 Comparison of technical specificities between SignBrain and the existing portable EEG devices

对比参数	SignBrain	Emotiv^[14]	美国 CGX^[15]	奥地利 g.tec^[16]
通道数量	16	14	20	16
采样频率 (Hz)	≥976	256	500	500
A/D位数	24	16	24	24
A/D分辨率 (μV)	0.53	0.51	—	0.53
阻抗	> 500 MΩ	—	—	> 100 MΩ
共模抑制比 (dB)	≥ 111	—	—	—
噪声 (uV)	≤ 1 μVpp	—	< 1 μVrms	< 0.6 μVrms
结构设计	爪形	爪形	爪形	脑电帽
重量	160 g	170 g	526 g	140 g

下载: 导出CSV

表 2 SignBrain设备与德国BP设备技术指标对比

Table 2 Comparison of technical specificities between SignBrain device and German BP device

对比参数	SignBrain	BrainAmp DC
通道数量	16	32
采样频率 (Hz)	976	5000
A/D分辨率 (μV)	0.53	0.1
输入动态范围μV(Vpp)	±17578	±16384
阻抗	实时阻抗	非实时阻抗
共模抑制比 (dB)	≥ 111	≥110
噪声 (uV)	≤ 1	< 1
电极材质	Ag/AgCL	Ag/AgCL

下载: 导出CSV

表 3 不同模型在SignBrain数据集上进行抑郁分类效果

Table 3 The performance of different models in classifying depression on the SignBrain dataset

Model	Acc	Pre	Rec	F1-score
DAN (Long et al. , 2015)	74.36	58.92	60.19	58.91
DANN (Ganin et al. , 2016)	74.37	65.92	63.09	62.88
ADA (Haeusser et al. , 2017)	80.47	69.37	68.81	67.95
DCANN (Ours)	82.73	70.61	69.42	68.67

下载: 导出CSV

表 4 视觉皮层O1通道诱发频率与刺激频率的误差

Table 4 The error between the induced frequency of the O1 channel and the stimulus frequency

刺激频率 (Hz)	10.8	13.6	15	20.6	22.7	24.1
实际诱发频率 (Hz)	10.75	13.58	15.03	20.65	22.64	24.07
误差 (%)	7.1	2.8	4.3	7.1	8.6	4.3

下载: 导出CSV

参考文献(20)

[1]	Mohamad Sawan, Muhammad T. Salam, Jérôme Le Lan, Amal Kassab, Sébastien Gélinas, Phetsamone Vannasing. Wireless Recording Systems: From Noninvasive EEG-NIRS to Invasive EEG Devices. IEEE Transactions on Biomedical Circuits and Systems, 2013, 7(2): 186−195. doi: 10.1109/TBCAS.2013.2255595
[2]	国家质量监督检验检疫总局, JJG 1043-2008, 脑电图机检定规程, 2008. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, JJG 1043-2008, Electroencephalographs, 2008.
[3]	国家药监局, GB 9706.226-2021 , 医用电气设备第2-26部分: 脑电图机的基本安全和基本性能专用要求, 2021. National Medical Products Administration, GB 9706.226-2021, Medical electrical equipment—Part 2-26: Particular requirements for the basic safety and essential performance of electroencephalographs.
[4]	“American electroencephalographic society guidelines for standard electrode position nomenclature,”Journal of Clinical Neurophysiology, vol. 8, pp. 200-202, 1991.
[5]	Greentek Company. Sintered Ag/AgCl Electrode [EB/OL]. https://www.medicalexpo.com.cn/prod/wuhan-greentek/product-118176-1004859.html.
[6]	Erwin Habibzadeh Tonekabony Shad, Marta Molinas, Trond Ytterdal. Impedance and Noise of Passive and Active Dry EEG Electrodes: A Review. IEEE Sensors Journal, DOI: 10.1109/JSEN.2020.3012394
[7]	Advantages of Nylon [Online], available: https://voltplastics.com/ about-us/ news-and-articles-page/66/advantages-of-nylon.
[8]	左年明, 蒋田仔. 穿戴式脑认知检测和调控无线闭环设备及方法. 中国: CN114916939B, 2022-11-08. Zuo Nian-Ming, Jiang Tian-Zi. Wireless wearable brain cognitive detection and control closed-loop device. China: CN114916939B, 2022-11-8
[9]	左年明, 张鑫, 蒋田仔, 张玉瑾, 刘浩. 一种可穿戴式光电同步脑活动无线监测设备. 中国: CN104207770A, 2017-02-15. Zuo Nian-Ming, Zhang Xin, Jiang Tian-Zi, Zhang Yu-Jin, Liu Hao. Wearable optoelectronic synchronous brain activity monitoring device. China: CN104207770A, 2017-02-15
[10]	左年明, 张鑫, 蒋田仔, 张玉瑾, 刘浩. 一种穿戴式无线脑血氧监测设备. 中国: CN204072104U, 2015-01-07. Zuo Nian-Ming, Zhang Xin, Jiang Tian-Zi, Zhang Yu-Jin, Liu Hao. Wearable wireless brain blood oxygen monitoring device. China: CN204072104U, 2015-01-07.
[11]	左年明, 张鑫, 蒋田仔, 陈良甫, 张玉瑾. 一种脑活动事件同步记录系统及方法. 中国: CN104287726B, 2016-08-24. Zuo Nian-Ming, Zhang Xin, Jiang Tian-Zi, Chen Liang-Fu, Zhang Yu-Jin. Brain activity event synchronous recording system and method. China: CN104287726B, 2016-08-24.
[12]	Jiang Tian-Zi, Zuo Nian-Ming, Zhang Xin, Zhang Yu-Jin, Liu Hao. Method for Storing Data of Photoelectrically Synchronous Brain Activity Recording, US 20170290524 A1, PCT/CN2014/086904, 2019-10-29
[13]	左年明, 蒋田仔, 孟庆桐. 基于数据裁剪的脑电数据蓝牙传输系统. 中国: CN114974497A, 2022-08-30. Zuo Nian-Ming, Jiang Tian-Zi, Meng Qing-Tong. Bluetooth transmission system of EEG data based on data clipping. China: CN114974497A, 2022-08-30.
[14]	Emotiv EPOC headset [Online], available: http: //www.emotiv.com, Dec 4, 2024
[15]	GCX headset[Online], available: https://www.cgxsystems.com, Dec 4, 2024
[16]	gtec headset[Online], available: https://www.gtec.at/product, Dec 4, 2024
[17]	BP BrainAmp DC [Online], available: https://www.brainproducts.com, Dec 4, 2024
[18]	姜婉清. 基于迁移学习的脑电信号情绪识别算法研究[硕士论文] 中国科学院大学, 2023 Jiang W. EEG Emotion Recognition Based on Transfer Learning [Master thesis] University of Chinese Academy of Sciences, CHINA , 2023.
[19]	Ganin Y, Ustinova E, Ajakan H, et al. Domain-adversarial training of neural networks. Journal of machine learning research, 2016, 17(59): 1−35.
[20]	Krzanowski, W. J. , Principles of Multivariate Analysis, Oxford University Press, Oxford, 1988.