A Miniaturized SSVEP Brain-Computer Interface System

CAI Yu; WANG Junyang; JIANG Chuanli; LUO Ruixin; LV Zhengchao; YU Haiqing; HUANG Yongzhi; JUNG Tzyy-Ping; XU Minpeng

doi:10.11999/JEIT251223

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CAI Yu, WANG Junyang, JIANG Chuanli, LUO Ruixin, LV Zhengchao, YU Haiqing, HUANG Yongzhi, JUNG Tzyy-Ping, XU Minpeng. A Miniaturized SSVEP Brain-Computer Interface System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251223

Citation:

CAI Yu, WANG Junyang, JIANG Chuanli, LUO Ruixin, LV Zhengchao, YU Haiqing, HUANG Yongzhi, JUNG Tzyy-Ping, XU Minpeng. A Miniaturized SSVEP Brain-Computer Interface System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251223

Citation:

CAI Yu, WANG Junyang, JIANG Chuanli, LUO Ruixin, LV Zhengchao, YU Haiqing, HUANG Yongzhi, JUNG Tzyy-Ping, XU Minpeng. A Miniaturized SSVEP Brain-Computer Interface System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251223

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A Miniaturized SSVEP Brain-Computer Interface System

doi: 10.11999/JEIT251223 cstr: 32379.14.JEIT251223

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)

Accepted Date: 2026-01-12
Rev Recd Date: 2026-01-12

Available Online: 2026-01-24

Abstract

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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References(20)

References

[1]	LIU Yingjie, ZHANG Ye, ZHONG Yifei, et al. Favoritism or bias? Cooperation and competition under different intergroup relationships: Evidence from EEG hyperscanning[J]. Cerebral Cortex, 2024, 34(4): bhae131. doi: 10.1093/cercor/bhae131.
[2]	CAROLLO A and ESPOSITO G. Hyperscanning literature after two decades of neuroscientific research: A scientometric review[J]. Neuroscience, 2024, 551: 345–354. doi: 10.1016/j.neuroscience.2024.05.045.
[3]	张力新, 周鸿展, 王东, 等. 脑机接口中脑电图-近红外光谱联合分析进展研究[J]. 电子与信息学报, 2024, 46(3): 790–797. doi: 10.11999/JEIT230257. ZHANG Lixin, ZHOU Hongzhan, WANG Dong, et al. Research progress of electroencephalography-near-infrared spectroscopy combined analysis in brain-computer interface[J]. Journal of Electronics & Information Technology, 2024, 46(3): 790–797. doi: 10.11999/JEIT230257.
[4]	MIHAJLOVIC V, GRUNDLEHNER B, VULLERS R, et al. Wearable, wireless EEG solutions in daily life applications: What are we missing?[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(1): 6–21. doi: 10.1109/JBHI.2014.2328317.
[5]	肖晓琳, 辛风然, 梅杰, 等. 自适应脑机接口研究综述[J]. 电子与信息学报, 2023, 45(7): 2386–2394. doi: 10.11999/JEIT220707. XIAO Xiaolin, XIN Fengran, MEI Jie, et al. A review of adaptive brain-computer interface research[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2386–2394. doi: 10.11999/JEIT220707.
[6]	CHEN Xiaogang, WANG Yijun, NAKANISHI M, et al. High-speed spelling with a noninvasive brain-computer interface[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(44): E6058–E6067. doi: 10.1073/pnas.1508080112.
[7]	LARSEN O F P, TRESSELT W G, LORENZ E A, et al. A method for synchronized use of EEG and eye tracking in fully immersive VR[J]. Frontiers in Human Neuroscience, 2024, 18: 1347974. doi: 10.3389/fnhum.2024.1347974.
[8]	RAO Zuguang, ZHU Junbiao, LU Zilin, et al. A wearable brain-computer interface with fewer EEG channels for online motor imagery detection[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 4143–4154. doi: 10.1109/TNSRE.2024.3502135.
[9]	NISO G, ROMERO E, MOREAU J T, et al. Wireless EEG: A survey of systems and studies[J]. NeuroImage, 2023, 269: 119774. doi: 10.1016/j.neuroimage.2022.119774.
[10]	CHUANG C H, LU Shaowei, CHAO Yiping, et al. Near-zero phase-lag hyperscanning in a novel wireless EEG system[J]. Journal of Neural Engineering, 2021, 18(6): 066010. doi: 10.1088/1741-2552/ac33e6.
[11]	DEPOLLI M, VERDEL N, and KOSEC G. Offline synchronization of signals from multiple wireless sensors[J]. IEEE Sensors Journal, 2025, 25(4): 7079–7094. doi: 10.1109/JSEN.2024.3519905.
[12]	ZENIL M S C, LÓPEZ A A, MENDOZA G R P, et al. Evaluation of communication protocols for medical device interoperability: BLE and ZigBee[C]. 2023 Mexican International Conference on Computer Science, Guanajuato, Mexico, 2023: 1–6. doi: 10.1109/enc60556.2023.10508654.
[13]	蔡雨, 许敏鹏, 钟子平, 等. 一种低功耗蓝牙通信的数据同步方法及电子设备[P]. 中国, 116133108A, 2023. CAI Yu, XU Minpeng, ZHONG Ziping, et al. Data synchronization method for low-power-consumption Bluetooth communication and electronic equipment[P]. CN, 116133108A, 2023.
[14]	MEI Jie, LUO Ruixin, XU Lichao, et al. MetaBCI: An open-source platform for brain-computer interfaces[J]. Computers in Biology and Medicine, 2024, 168: 107806. doi: 10.1016/j.compbiomed.2023.107806.
[15]	MEHDIZAVAREH M H, HEMATI S, and SOLTANIAN-ZADEH H. Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs[J]. PLoS One, 2020, 15(1): e0226048. doi: 10.1371/journal.pone.0226048.
[16]	陈强, 陈勋, 余凤琼. 基于独立向量分析的脑电信号中肌电伪迹的去除方法[J]. 电子与信息学报, 2016, 38(11): 2840–2847. doi: 10.11999/JEIT160209. CHEN Qiang, CHEN Xun, and YU Fengqiong. Removal of muscle artifact from EEG data based on independent vector analysis[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2840–2847. doi: 10.11999/JEIT160209.
[17]	WOLPAW J R, BIRBAUMER N, MCFARLAND D J, et al. Brain-computer interfaces for communication and control[J]. Clinical Neurophysiology, 2002, 113(6): 767–791. doi: 10.1016/S1388-2457(02)00057-3.
[18]	李晓东, 曹翔, 王俊霖, 等. 可穿戴式稳态视觉诱发电位脑机接口在现实场景下的性能评估[J]. 生物医学工程学杂志, 2025, 42(3): 464–472. doi: 10.7507/1001-5515.202310069. LI Xiaodong, CAO Xiang, WANG Junlin, et al. Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario[J]. Journal of Biomedical Engineering, 2025, 42(3): 464–472. doi: 10.7507/1001-5515.202310069.
[19]	朱艺森, 季洲宇, 李舒然, 等. 面向智慧医疗的便携式稳态视觉诱发电位脑机接口系统[J]. 生物医学工程学杂志, 2025, 42(3): 455–463. doi: 10.7507/1001-5515.202412051. ZHU Yisen, JI Zhouyu, LI Shuran, et al. A portable steady-state visual evoked potential brain-computer interface system for smart healthcare[J]. Journal of Biomedical Engineering, 2025, 42(3): 455–463. doi: 10.7507/1001-5515.202412051.
[20]	XIONG Bang, WAN Bo, HUANG Jiayang, et al. Joint frequency-phase-chirp modulation of high-frequency VEPs towards user-friendly and high-capacity BCIs[J]. Biomedical Signal Processing and Control, 2026, 113: 109122. doi: 10.1016/j.bspc.2025.109122.