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| Development of a Highly Integrated Brain-Computer Interface Protective Helmet Based onDetachable Flexible Comb-Shaped Sponge Electrodes |
| Yuan Xuezhi1, Li Hui1, Tang Qi2, Chen Jie2, Wang Kun1, Xiao Xiaolin1, Xu Minpeng1#*, Fan Yi2, Bai Ru2, Wang Junlei2 |
1(Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China) 2(Hubei Huaqiang Technology Co., Ltd., Yichang 443000, Hubei, China) |
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Abstract Aiming to solve core problems faced in the integration of brain-computer interface (BCI) and protective helmets, such as portable electrode replacement, small hardware anti-interference, stable signal transmission and wearing comfort, this paper proposed an innovative solution of "integration of detachable flexible comb-shaped sponge electrodes and highly integrated amplifiers". The electrode used an elastic sponge-flexible silicone substrate, flexible metal wires, and a comb-shaped distributed layout, improving comfort while ensuring signal quality. The amplifier, with high-integration components and a simplified circuit, achieved a volume of 6.6 cm×5.2 cm×1.2 cm, a common-mode rejection ratio exceeding 110 dB, a system battery life of 29.3 hours, and a 0.1%~0.2% packet loss rate. Wearing comfort evaluation and preparation time tests on 10 subjects showed an average preparation time of (2.28±0.48)minutes, demonstrating good usability and applicability. The unmanned aerial vehicle (UAV) control experiment based on steady-state visual evoked potential (SSVEP) verified that the average control accuracy for the 10 subjects reached 90.88%±2.61%. The system was able to operate stably and reliably, providing an effective technical path for the deep integration of BCI and protective equipment.
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Received: 20 December 2025
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Corresponding Authors:
*E-mail: xmp52637@tju.edu.cn
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| About author:: #Member,Chinese Society of Biomedical Engineering |
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[1] 彭坚,王雪鹏,赵丹华,等. 单兵智能头盔在消防救援中的探索与应用[J]. 包装工程, 2022, 43(12): 1-14. [2] 熊楠. 智能安全头盔在电力施工现场安全保障中的应用探索[J].信息与电脑(理论版), 2024, 36(13): 19-21. [3] 戴廷飞,刘邈,叶阳阳,等. 人机共享控制机器人系统的应用与发展[J]. 仪器仪表学报, 2019, 40(3): 62-73. [4] 陆诗雨. 基于具身认知理论的脑电头盔设备设计研究[D]. 上海:华东理工大学,2025. [5] 俞青青,邢更力. 我国无人机在应急救援中的应用与发展[J]. 职业卫生与应急救援, 2021, 39(3): 350-355. [6] 郝璐瑶,刘宇栋,尹赫,等. 实时检测监控式防毒面罩的构思与研发[J]. 科技视界, 2021, (27): 57-59. [7] 彭宇. 健康监测头盔结构设计与优化研究[D]. 长沙:中南大学,2024. [8] Patrique F, Richard M, Stefan G, et al. Contact pressure and flexibility of multipin dry EEG electrodes[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(4):750-757. [9] 赵慧庆. 便捷式耳电极制备及其在脑机接口中的应用[D]. 北京:北京印刷学院,2023. [10] 侍聪聪. 基于头盔式电极帽的脑电无线采集系统设计与实现[D]. 上海:上海电机学院,2022. [11] 王桂英,王艳娟. 脑电采集用电极制造技术研究进展[J]. 科学技术与工程, 2018, 18(25): 107-115. [12] Lopez A, Richardson PC. Capacitive electrocardiographic and bioelectric electrodes[J]. IEEE Transactions on Biomedical Engineering, 1969, 16(1): 99. [13] Ko WH, Hynecek J. Dry electrodes and electrode amplifiers[C]// Biomedical Electrode Technology: Theory and Practice. New York: Plenum Press, 1974: 169-181. [14] De Luca CJ, Le Fever RS, Stulen FB. Pasteless electrode for clinical use[J]. Medical and Biological Engineering and Computing, 1979, 17(3): 387-390. [15] 王晓韡,石立臣,吕宝粮. 干电极脑电采集技术综述[J]. 中国生物医学工程学报, 2010, 29(5): 777-784. [16] Hairston WD, Whitaker KW, Ries AJ, et al. Usability of four commercially-oriented EEG systems[J]. Journal of Neural Engineering, 2014, 11(4): 046018. [17] 李广利,刘熊浩岚,陈娜羽,等. 面向无创脑-机接口的耳脑电电极研究进展[J]. 天津工业大学学报, 2025, 44: 80-90. [18] 谢士遥,汤佳贝,蔡雨,等. 脑电BCI系统的软硬件开发平台发展现状[J]. 电子测量与仪器学报, 2022, 36(6): 1-12. [19] 贺庆,郝思聪,司娟宁,等. 面向脑机接口的脑电采集设备硬件系统综述[J]. 中国生物医学工程学报, 2020, 39: 747-758. [20] 管生智. 肌电信号采集系统设计及手势动作识别算法研究[D]. 北京:北方工业大学,2025. [21] 赖义汉,王凯. 基于MPU6050的双轮平衡车控制系统设计[J]. 河南工程学院学报(自然科学版), 2014, 26(1): 53-57. [22] 秦代友. 睡眠中个体化慢波识别算法及其应用研究[D]. 成都:电子科技大学,2025. [23] 蒲超. 基于FPGA的SSVEP专用脑机接口系统[D]. 成都:电子科技大学,2024. [24] 欧阳磊. 基于自整定PID控制器的温度控制系统研究[D]. 淮南:安徽理工大学,2009. [25] 王越. 扫频OCT数据采集处理中的k时钟系统[D]. 重庆:重庆理工大学,2025. [26] 黎芳成. 基于SSVEP脑电智能轮椅的研究与试验[D]. 重庆:重庆理工大学,2023. [27] Niso G, Romero E, Moreau JT, et al. Wireless EEG: a survey of systems and studies[J]. NeuroImage, 2023, 269: 119774. [28] 乐航宇. 多通道脑电采集及应用系统的研发[D]. 深圳:深圳大学,2023. [29] 施锦河. 运动想象脑电信号处理与P300刺激范式研究[D]. 杭州:浙江大学,2012. [30] 赵重阳,王俊峰,陈世强,等. 网络性能测量系统架构的研究与设计[J]. 现代计算机(专业版), 2013(33): 3-7. [31] 王圣哲. 基于SSVEP的脑控智能车系统的研究与实现[D]. 济南:山东交通学院,2025. [32] 郭孟澳,杨帮华,耿亦婷,等. 基于增强现实和稳态视觉诱发电位的视觉目标检测系统[J]. 生物医学工程学杂志, 2024, 41(4): 684-691. [33] 于静静,邓咏梅. 基于生理监测的可穿戴智能服装应用与展望[J].纺织科技进展, 2021 (2): 6-9,15. [34] Wang Longfei, Liu Jinguan, Yang Bin, et al. PDMS-based low cost flexible dry electrode for long-term EEG measurement[J]. IEEE Sensors Journal, 2012, 12(9): 2898-2904. [35] 张发华,舒琳,邢晓芬. 头皮脑电采集技术研究[J].电子技术应用, 2017, 43(12): 3-8. |
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