一种微型化SSVEP脑机接口系统

蔡雨; 王俊洋; 姜传力; 罗睿心; 吕正超; 于海情; 黄永志; 钟子平; 许敏鹏

doi:10.11999/JEIT251223

一种微型化SSVEP脑机接口系统

doi: 10.11999/JEIT251223 cstr: 32379.14.JEIT251223

蔡雨^{1, 2},
王俊洋^{1, 2},
姜传力^{1, 2},
罗睿心^{1, 2},
吕正超^{1, 2},
于海情^{1, 2},
黄永志^{1, 2},
钟子平^{1, 3},
许敏鹏^{1, 2, ,}

1.
天津大学医学工程与转化医学研究院天津 300072
2.
脑机交互与人机共融海河实验室天津 300392
3.
加州大学斯沃茨计算科学中心美国加利福尼亚 92093

基金项目: 国家重点研发计划(项目编号：2023YFF1203701)

详细信息

作者简介:
蔡雨：男，博士生，研究方向为脑机接口硬件

王俊洋：男，博士生，研究方向为脑机接口

姜传力：男，博士生，研究方向为脑机接口硬件

罗睿心：女，博士生，研究方向为脑机接口

吕正超：男，博士生，研究方向为脑机接口硬件

于海情：女，正高级工程师，研究方向为脑-机接口、神经信号处理、脑际神经科学

黄永志：男，英才副教授，研究方向为神经信号处理、脑机接口、侵入式神经调控技术

钟子平：男，教授，研究方向为脑机接口

许敏鹏：男，讲席教授，研究方向为脑机接口、神经信号处理和神经调控

通讯作者:
许敏鹏　xmp52637@tju.edu.cn

中图分类号: R741.044
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- 文章访问数: 72
- HTML全文浏览量: 30
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- 被引次数: 0
出版历程
- 修回日期: 2026-01-12
- 录用日期: 2026-01-12
- 网络出版日期: 2026-01-24

A Miniaturized SSVEP Brain-Computer Interface System

CAI Yu^{1, 2},
WANG Junyang^{1, 2},
JIANG Chuanli^{1, 2},
LUO Ruixin^{1, 2},
LV Zhengchao^{1, 2},
YU Haiqing^{1, 2},
HUANG Yongzhi^{1, 2},
JUNG Tzyy-Ping^{1, 3},
XU Minpeng^{1, 2
, ,}

1.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
2.
Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, China
3.
University of California, Swartz Center for Computational Sciences, California 92093, USA

Funds: National Key Research and Development Program of China (Grant No. 2023YFF1203701)

摘要

摘要: 脑机接口(Brain-Computer Interface, BCI)正从实验室走向日常应用，其发展的核心瓶颈在于如何在不依赖笨重设备和同步线缆的前提下，实现高性能的脑电采集。现有无线系统难以在同步精度与系统微型化、无硬件束缚之间取得兼顾。为此，该文研制了一种采集端微型化且无需同步线缆的稳态视觉诱发电位(Steady-State Visual Evoked Potential, SSVEP)脑机接口系统。该系统采用分布式微型节点架构，将重量仅3.7 g、体积仅为3.05 cm³的微型采集节点隐蔽佩戴于头发间。在无需专用同步硬件、仅使用少量电极、且在非屏蔽普通室内环境下，搭建了40指令的在线SSVEP解码系统。结果显示，系统达到了(95.00±2.04)%的识别准确率与(147.24±30.52) bits/min的峰值信息传输速率。该研究为开发真正可穿戴的下一代脑机接口提供了可行的系统级解决方案。
- 脑机接口 /
- 稳态视觉诱发电位 /
- 可穿戴系统 /
- 微型化 /
- 分布式采集
Abstract: Objective The practical deployment of brain-computer interface (BCI) systems in daily-life scenarios is constrained by the bulkiness of acquisition hardware and the tethering cables required for reliable operation. While portable systems have been developed, achieving concurrent goals of significant device compactness, complete user mobility, and high decoding performance remains a challenge. This study aims to design, implement, and validate a wearable steady-state visual evoked potential (SSVEP) BCI system. The primary goal is to realize an integrated system featuring ultra-miniaturized, concealable acquisition hardware and a stable architecture that operates without the need for synchronization cables, and to demonstrate that this approach delivers online performance comparable to conventional laboratory systems, thereby advancing the feasibility of truly wearable BCIs. Methods A system-level solution was developed, centered on a distributed architecture to achieve wearability and hardware simplification. The core of the system is an ultra-miniaturized acquisition node. Each node, functioning as an independent EEG acquisition unit, integrates a Bluetooth Low Energy (BLE) system-on-chip (CC2640R2F), a high-precision analog-to-digital converter (ADS1291), a battery, and an electrode into a single encapsulated module. Through optimized 6-layer PCB design and a stacked assembly, the module dimensions were reduced to 15.12 mm × 14.08 mm × 14.31 mm (3.05 cm³) with a weight of 3.7 g. Each node incorporates a single active electrode, and all nodes share a common reference electrode connected via a thin, short wire. This design reduces scalp connections and enables a hair-clip structure for concealed placement within the user's hair. Multiple such nodes form a star network coordinated by a master device, which manages communication with a stimulus-presentation computer.To enable cable-free operation while maintaining data integrity, a synchronization strategy was implemented to address timing uncertainties inherent in distributed wireless systems. This strategy combines hardware-event detection with software-based clock management to align stimulus markers with the multi-channel EEG data streams without dedicated synchronization cables. The master device coordinates this process and streams the synchronized data to the computer for real-time processing.System evaluation was conducted in two phases. Foundational performance metrics included physical characteristics, key electrical parameters (input-referred noise: 3.91 μV_pp; common-mode rejection ratio: 132.99 dB), and synchronization accuracy across different network scales. Application-level performance was assessed through a 40-command online SSVEP spelling experiment with six subjects in an unshielded room with common RF interference. Four nodes were placed at positions Pz, PO3, PO4, and Oz. EEG epochs (0.14–3.14 s post-stimulus) were analyzed using canonical correlation analysis (CCA) and ensemble task-related component analysis (e-TRCA) to compute recognition accuracy and information transfer rate (ITR). Results and Discussions The implemented system successfully achieved its design objectives. Each acquisition node attained an ultra-compact form factor (3.05 cm³, 3.7 g) suitable for concealed wear, with a battery life exceeding 5 hours at a 1000 Hz sampling rate. The electrical performance confirmed its capability for high-quality SSVEP acquisition.The cable-free synchronization strategy provided the necessary temporal stability for system operation. Evaluation showed that over 95% of event markers were aligned with the EEG data stream with an error of less than 1 millisecond (Fig. 4), meeting the requirements for SSVEP-BCI applications. This reliable synchronization contributed to the quality of the recorded neural signals. Grand-averaged SSVEP responses across subjects exhibited clear and stable waveforms with precise phase alignment (Fig. 5). The signal-to-noise ratio at the fundamental stimulation frequency exceeded 10 dB for all 40 commands (Fig. 6), confirming good signal quality.In the online spelling experiment, the system demonstrated robust decoding performance. Using the e-TRCA algorithm with a 3-second data window, an average recognition accuracy of (95.00 ± 2.04)% was achieved. The system reached a peak ITR of (147.24 ± 30.52) bits/min with a short 0.4-second data length (Fig. 7). A comparative analysis with existing SSVEP-BCI systems (Table 1) shows that the proposed system, under constraints of miniaturization, cable-free use, and a reduced number of electrodes (four channels), achieved accuracy comparable to some cable-dependent laboratory systems while demonstrating improved wearability. Conclusions This work presents the development and validation of a wearable SSVEP-BCI system that integrates ultra-miniaturized hardware with a distributed, cable-free architecture. The system demonstrates that through coordinated design at the hardware and system levels, it is possible to overcome traditional trade-offs between device size, user freedom, and decoding capability. The acquisition node, at 3.7 g and 3.05 cm³, represents a significant step toward concealable wearability. The implemented synchronization strategy supported reliable operation without dedicated cables. The overall system, evaluated in a realistic environment, delivered online performance competitive with many cable-dependent setups, achieving 95.00% recognition accuracy and a peak ITR of 147.24 bits/min in a 40-target task. Therefore, this study provides a comprehensive system-level solution, contributing a practical platform that facilitates the transition of high-performance BCIs from the laboratory toward everyday wearable applications.
- Brain-Computer Interface (BCI) /
- Steady-State Visual Evoked Potential (SSVEP) /
- Wearable System /
- Miniaturization /
- Distributed Acquisition

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图 1 系统硬件架构图

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图 8 SSVEP-BCI系统采集设备图

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图 2 同步方法时序流程图

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图 3 性能测试系统结构框图

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图 4 一主多从架构下各设备事件标记误差分布与累积分布函数

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图 5 6名受试者4导联平均后的10 Hz SSVEP响应特性

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图 6 40指令SSVEP跨受试者平均信噪比

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图 7 40指令SSVEP跨受试者平均解码性能

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表 1 不同SSVEP-BCI系统的性能对比

序号	研究	设备	体积(cm³)；重量(g)	输入噪声 (μV_pp)	同步方式/精度(ms)	指令数	使用电极数量(个)	算法:ITR(bits/min)(峰值时间(s))；正确率(%)(样本时长(s))
1	文献[6]	Synamps2 商用台式	体积834；重量1500 (仅头盒)	0.5	TTL/-	40	9(64导)	CCA:267(0.5 s)； 91.04%(0.5 s)
2	文献[18]	ESPW308 商用便携式	-/-	-	未提及/-	40	4	OACCA:98.46(1.4 s)； 91.40%(3.0 s)
3	文献[19]	LinkMeR 商用便携式	体积-；重量75(仅采集主机)	-	未提及/<1	9	8	FBCCA:47.01(2.0 s)； 82.22%(3.0 s)
4	文献[20]	iRecorder W8 商用便携式	体积116；重量110 (仅采集主机)	<1	额外硬件/≤1	48	8	FBCCA-C:126.11(1.6 s)； 85.49%(3 s)
5	本研究	自研隐蔽式	体积12.2；重量16.3(4个采集设备+参考电极)	3.91	无线/≤1	40	4(可扩展8)	eTRCA:147.24(0.4 s)； 95.00%(3.0 s)
注：对比设备的体积与重量数据为其采集主机参数，未包含电极、脑电帽等佩戴套件；本研究数据为四个采集从设备与参考电极的总和。

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