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

基金项目: 四川省科技计划项目(2022YFSY0014(2022-2024)), 中国科学院先导专项(QYZDJSSW-SMC019), 科技部科技创新2030-脑科学与类脑智能项目(2021ZD0200200)资助

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. 主要研究方向为脑信号检测, 软件研发和脑电分析算法设计.E-mail: 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. 主要研究方向为脑影像分析, 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. 主要研究方向为可穿戴脑机接口应用. 本文通信作者.E-mail: nmzuo@nlpr.ia.ac.cn

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出版历程
- 收稿日期: 2024-08-19
- 网络出版日期: 2025-03-26
- 刊出日期: 2025-05-20

SignBrain: Wireless Wearable EEG Technology

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

Funds: Supported by Science and Technology Plan Program of Sichuan Province (2022YFSY0014(2022-2024)), Strategic Priority Research Program of the Chinese Academy of Sciences‌‌ (QYZDJSSW-SMC019), and Brain Science and Brain-Inspired Intelligence Project under the National Science and Technology Innovation 2030 Initiative (2021ZD0200200)

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 covers brain imaging analysis, brainnetome, digital twin brain and clinical applications

ZUO Nian-Ming　Professor at the Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. His main research interest is wearable brain-computer interface and its applications. Corresponding author of this paper

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“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 self-developed wireless wearable non-invasive electroencephalogram (EEG) technology: 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 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 device does not require conductive gel and can be worn at any time. 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, application programming interface 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 clinical hospitals and related units. The reliability and stability of the device have been verified through closed-eye imagination writing experiments and broad frequency steady state visually evoked potential 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 /
- wearable /
- non-invasive /
- brain-computer interface

HTML全文

图 1 SignBrain设备性能检测报告

Fig. 1 SignBrain device performance test report

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图 2 设备主干结构

Fig. 2 Device backbone structure

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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

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图 4 设备爪形结构

Fig. 4 Claw structure of the device

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图 5 万向抱紧件

Fig. 5 Universal clamping component

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图 6 短中长三种海绵规格展示

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

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图 7 SignBrain设备效果图

Fig. 7 SignBrain device effect picture

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图 8 信号流程图

Fig. 8 Signal flow illustration

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图 9 SignBrain与Emotiv的频谱图对比

Fig. 9 Spectrogram comparison between SignBrain and Emotiv

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图 10 SignBrain与BP的频谱图对比

Fig. 10 Spectrogram comparison between SignBrain and BP

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图 11 实际应用测试图

Fig. 11 Practical application test

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图 12 5名男性被试

Fig. 12 5 male participants

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图 13 5名女性被试

Fig. 13 5 female participants

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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

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图 15 想象写字范式图

Fig. 15 Imagine writing pattern diagram

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图 16 26字母分类结果

Fig. 16 26 letter classification results

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图 17 被试使用SignBrain设备想象字母

Fig. 17 Participants used the SignBrain device toimagine letters

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图 18 采用BP设备与SignBrain设备想象写字模型训练结果

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

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图 19 SignBrain 设备6 个频率点视觉诱发O1位置脑电信号FFT图

Fig. 19 The FFT graph of O1 EEG at six frequency points by SSVEP using SignBrain device

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表 1 SignBrain与现有厂商便携脑电设备的技术指标对比

Table 1 Comparison of technical index between SignBrain and the existing manufacture 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(MΩ)	> 500	—	—	> 100
共模抑制比(dB)	≥ 111	—	—	—
噪声	≤ 1 μVpp	—	< 1 μVrms	< 0.6 μVrms
结构设计	爪形	爪形	爪形	脑电帽
重量(g)	160	170	526	140

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表 2 SignBrain设备与德国BP设备技术指标对比

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

对比参数	SignBrain	BP
通道数量	16	32
采样频率(Hz)	976	5 000
A/D分辨率(μV)	0.53	0.10
输入动态范围(μVpp)	±17578	±16384
阻抗	实时阻抗	非实时阻抗
共模抑制比(dB)	≥ 111	≥ 110
噪声(μV)	≤ 1	< 1
电极材质	Ag/AgCL	Ag/AgCL

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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

模型	Acc	Pre	Rec	F1-score
DAN^[19]	74.36	58.92	60.19	58.91
DANN^[20]	74.37	65.92	63.09	62.88
ADA^[21]	80.47	69.37	68.81	67.95
DCANN (Ours)	82.73	70.61	69.42	68.67

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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	15.0	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

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