Citation: | WEI Wei, QIU Shuang, LI Xujin, MAO Jiayu, WANG Yanzi, HE Huiguang. A Review of Research Progress on Brain-Computer Interface Systems for Rapid Serial Visual Presentation Based on ElectroEncephaloGram[J]. Journal of Electronics & Information Technology, 2024, 46(2): 443-455. doi: 10.11999/JEIT230952 |
[1] |
WOLPAW J R, WOLPAW E W, 伏云发, 杨秋红, 徐保磊, 等译. 脑-机接口: 原理与实践[M]. 北京: 国防工业出版社, 2017: 3.
WOLPAW J R, WOLPAW E W, FU Yunfa, YANG Qiuhong, XU Baolei, et al. translation. Brain-Computer Interface: Principles and Practice[M]. Beijing: National Defense Industry Press, 2017: 3.
|
[2] |
GERSON A D, PARRA L C, and SAJDA P. Cortically coupled computer vision for rapid image search[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14(2): 174–179. doi: 10.1109/TNSRE.2006.875550.
|
[3] |
LEES S, DAYAN N, CE - computer interfaces[J]. Journal of Neural Engineering, 2018, 15(2): 021001. doi: 10.1088/1741-2552/COTTI H, et al. A review of rapid serial visual presentation-based brainaa9817.
|
[4] |
LUCK S J, 范思陆, 丁玉珑, 曲折, 等译. 事件相关电位基础[M]. 上海: 华东师范大学出版社, 2009: 6.
LUCK S J, FAN Silu, DING Yulong, QU Zhe, et al. translation. Introduction to the Event-Related Potential Technique[M]. Shanghai: East China Normal University Press, 2009: 6.
|
[5] |
YI Weibo, QIU Shuang, FAN Xinan, et al. Estimation of mental workload induced by different presentation rates in rapid serial visual presentation tasks[C]. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Beilin, Germany, 2019: 5552–5555. doi: 10.1109/EMBC.2019.8857274.
|
[6] |
CECOTTI H. Single-trial detection with magnetoencephalography during a dual-rapid serial visual presentation task[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(1): 220–227. doi: 10.1109/TBME.2015.2478695.
|
[7] |
LIN Zhimin, ZHANG Chi, ZENG Ying, et al. A novel P300 BCI speller based on the Triple RSVP paradigm[J]. Scientific Reports, 2018, 8(1): 3350. doi: 10.1038/s41598-018-21717-y.
|
[8] |
LIN Zhimin, ZENG Ying, WANG Xiaojuan, et al. EEG-based target detection during a multi-rapid serial visual presentation[C]. 8th International IEEE/EMBS Conference on Neural Engineering (NER), Shanghai, China, 2017: 556–559. doi: 10.1109/NER.2017.8008412.
|
[9] |
ZHANG Shangen, CHEN, Xiaogang, WANG Yijun, et al. Visual field inhomogeneous in brain–computer interfaces based on rapid serial visual presentation[J]. Journal of Neural Engineering, 2022, 19(1): 016015. doi: 10.1088/1741-2552/ac4a3e.
|
[10] |
MATRAN-FERNANDEZ A and POLI R. Brain–computer interfaces for detection and localization of targets in aerial images[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(4): 959–969. doi: 10.1109/TBME.2016.2583200.
|
[11] |
WON D O, HWANG H J, KIM D M, et al. Motion-based rapid serial visual presentation for gaze-independent brain-computer interfaces[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(2): 334–343. doi: 10.1109/TNSRE.2017.2736600.
|
[12] |
MIJANI A M, SHAMSOLLAHI M B, and HASSANI M S. A novel dual and triple shifted RSVP paradigm for P300 speller[J]. Journal of Neuroscience Methods, 2019, 328: 108420. doi: 10.1016/j.jneumeth.2019.108420.
|
[13] |
JALILPOUR S, SARDOUIE S H, and MIJANI A. A novel hybrid BCI speller based on RSVP and SSVEP paradigm[J]. Computer Methods and Programs in Biomedicine, 2020, 187: 105326. doi: 10.1016/j.cmpb.2020.105326.
|
[14] |
RIVET B, SOULOUMIAC A, ATTINA V, et al. xDAWN algorithm to enhance evoked potentials: Application to brain–computer interface[J]. IEEE Transactions on Biomedical Engineering, 2009, 56(8): 2035–2043. doi: 10.1109/TBME.2009.2012869.
|
[15] |
BARACHANT A and CONGEDO M. A plug&play P300 BCI using information geometry[EB/OL]. https://arxiv.org/abs/1409.0107, 2014.
|
[16] |
MANOR R and GEVA A B. Convolutional neural network for multi-category rapid serial visual presentation BCI[J]. Frontiers in Computational Neuroscience, 2015, 9: 168707. doi: 10.3389/fncom.2015.00146.
|
[17] |
LAWHERN V J, SOLON A J, WAYTOWICH N R, et al. EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces[J]. Journal of Neural Engineering, 2018, 15(5): 056013. doi: 10.1088/1741-2552/aace8c.
|
[18] |
XIAO Xiaolin, XU Minpeng, JIN Jing, et al. Discriminative canonical pattern matching for single-trial classification of ERP components[J]. IEEE Transactions on Biomedical Engineering, 2020, 67(8): 2266–2275. doi: 10.1109/TBME.2019.2958641.
|
[19] |
CUI Yujie, XIE Songyun, XIE Xinzhou, et al. LDER: A classification framework based on ERP enhancement in RSVP task[J]. Journal of Neural Engineering, 2023, 20(3): 036029. doi: 10.1088/1741-2552/acd95d.
|
[20] |
LI Bowen, ZHANG Shangen, HU Yijun, et al. Assembling global and local spatial-temporal filters to extract discriminant information of EEG in RSVP task[J]. Journal of Neural Engineering, 2023, 20(1): 016052. doi: 10.1088/1741-2552/acb96f.
|
[21] |
LI Bowen, LIN Yanfei, GAO Xiaorong, et al. Enhancing the EEG classification in RSVP task by combining interval model of ERPs with spatial and temporal regions of interest[J]. Journal of Neural Engineering, 2021, 18(1): 016008. doi: 10.1088/1741-2552/abc8d5.
|
[22] |
SHAN Hongchang, LIU Yu, and STEFANOV T. A simple convolutional neural network for accurate P300 detection and character spelling in brain computer interface[C]. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018: 1604–1610.
|
[23] |
SANTAMARIA-VAZQUEZ E, MARTINEZ-CAGIGAL V, VAQUERIZO-VILLAR F, et al. EEG-inception: A novel deep convolutional neural network for assistive ERP-based brain-computer interfaces[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(12): 2773–2782. doi: 10.1109/TNSRE.2020.3048106.
|
[24] |
MA Ronghua, YU Tianyou, ZHONG Xiaoli, et al. Capsule network for ERP detection in brain-computer interface[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 718–730. doi: 10.1109/TNSRE.2021.3070327.
|
[25] |
MAO Jiayu, QIU Shuang, WEI Wei, et al. Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection[J]. Neural Networks, 2023, 161: 65–82. doi: 10.1016/j.neunet.2023.01.009.
|
[26] |
ZANG Boyu, LIN Yanfei, LIU Zhiwei, et al. A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs[J]. Journal of Neural Engineering, 2021, 18(4): 0460c8. doi: 10.1088/1741-2552/ac1610.
|
[27] |
LI Fu, WANG Chong, LI Yang, et al. Phase preservation neural network for electroencephalography classification in rapid serial visual presentation task[J]. IEEE Transactions on Biomedical Engineering, 2022, 69(6): 1931–1942. doi: 10.1109/TBME.2021.3130917.
|
[28] |
JAYARAM V, ALAMGIR M, ALTUN Y, et al. Transfer learning in brain-computer interfaces[J]. IEEE Computa tional Intelligence Magazine, 2016, 11(1): 20–31. doi: 10.1109/MCI.2015.2501545.
|
[29] |
SHAMWELL J, LEE H, KWON H, et al. Single-trial EEG RSVP classification using convolutional neural networks[C]. Proceedings of the SPIE 9836, Micro-and Nanotechnology Sensors, Systems, and Applications VIII, Baltimore, USA, 2016: 983622. doi: 10.1117/12.2224172.
|
[30] |
HAJINOROOZI M, MAO Zijin, LIN Yuanpin, et al. Deep transfer learning for cross-subject and cross-experiment prediction of image rapid serial visual presentation events from EEG data[C]. 11th International Conference on Augmented Cognition, Vancouver, Canada, 2017: 45–55. doi: 10.1007/978-3-319-58628-1_4.
|
[31] |
MIJANI A M, EINIZADE A, SHAMSOLLAHI M B, et al. Cross-subject and cross-paradigm learning using convolutional neural network for P300 event-related potential detection[J]. Journal of Neurology and Neuroscience, 2020, 11(5): 329. doi: 10.36648/2171-6625.11.1.329.
|
[32] |
ZHANG Wen and WU Dongrui. Manifold embedded knowledge transfer for brain-computer interfaces[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(5): 1117–1127. doi: 10.1109/TNSRE.2020.2985996.
|
[33] |
WEI Wei, QIU Shuang, MA Xuelin, et al. Reducing calibration efforts in RSVP tasks with multi-source adversarial domain adaptation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(11): 2344–2355. doi: 10.1109/TNSRE.2020.3023761.
|
[34] |
FAN Liangwei, SHEN Hui, XIE Fengyu, et al. DC-tCNN: A deep model for EEG-based detection of dim targets[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1727–1736. doi: 10.1109/TNSRE.2022.3184725.
|
[35] |
SHE Qingshan, CAI Yinhao, DU Shengzhi, et al. Multi-source manifold feature transfer learning with domain selection for brain-computer interfaces[J]. Neurocomputing, 2022, 514: 313–327. doi: 10.1016/j.neucom.2022.09.124.
|
[36] |
WAYTOWICH N R, LAWHERN V J, BOHANNON A W, et al. Spectral transfer learning using information geometry for a user-independent brain-computer interface[J]. Frontiers in Neuroscience, 2016, 10: 430. doi: 10.3389/fnins.2016.00430.
|
[37] |
LEE J, WON K, KWON M, et al. CNN with large data achieves true zero-training in online P300 brain-computer interface[J]. IEEE Access, 2020, 8: 74385–74400. doi: 10.1109/ACCESS.2020.2988057.
|
[38] |
WEI Wei, QIU Shuang, ZHANG Yukun, et al. ERP prototypical matching net: A meta-learning method for zero-calibration RSVP-based image retrieval[J]. Journal of Neural Engineering, 2022, 19(2): 026028. doi: 10.1088/1741-2552/ac5eb7.
|
[39] |
LI Xujin, QIU Shuang, WEI Wei, et al. A zero-training method for RSVP-based brain computer interface[C]. Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Shenzhen, China, 2022: 113–125. doi: 10.1007/978-3-031-18910-4_10.
|
[40] |
LI Xujin, WEI Wei, QIU Shuang, et al. TFF-Former: Temporal-frequency fusion transformer for zero-training decoding of two BCI tasks[C]. Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal, 2022: 51–59. doi: 10.1145/3503161.3548269.
|
[41] |
MANOR R, MISHALI L, and GEVA A B. Multimodal neural network for rapid serial visual presentation brain computer interface[J]. Frontiers in Computational Neuroscience, 2016, 10: 130. doi: 10.3389/fncom.2016.00130.
|
[42] |
WU Qunjian, ZENG Ying, ZHANG Chi, et al. An EEG-based person authentication system with open-set capability combining eye blinking signals[J]. Sensors, 2018, 18(2): 335. doi: 10.3390/s18020335.
|
[43] |
DING Yi, HUYNH B, XU Aiwen, et al. Multimodal classification of EEG during physical activity[C]. International Conference on Multimodal Interaction, Suzhou, China, 2019, 185–194. doi: 10.1145/3340555.3353759.
|
[44] |
MATRAN-FERNANDEZ A and POLI R. Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces[J]. PLoS One, 2017, 12(5): e0178498. doi: 10.1371/journal.pone.0178498.
|
[45] |
ZHENG Li, SUN Sen, ZHAO Hongze, et al. A cross-session dataset for collaborative brain-computer interfaces based on rapid serial visual presentation[J]. Front Neuroscience, 2020, 14: 579469. doi: 10.3389/fnins.2020.579469.
|
[46] |
ZHANG Hangkui, ZHU Li, XU Senwei, et al. Two brains, one target: Design of a multi-level information fusion model based on dual-subject RSVP[J]. Journal of Neuroscience Methods, 2021, 363: 109346. doi: 10.1016/j.jneumeth.2021.109346.
|
[47] |
ZHAO Ziwei, LIN Yanfei, WANG Yijun, et al. Single-trial EEG classification using spatio-temporal weighting and correlation analysis for RSVP-based collaborative brain computer interface[J]. Transactions on Biomedical Engineering, 2023. doi: 10.1109/TBME.2023.3309255.
|
[48] |
XU Meng, CHEN Yuanfang, WANG Dan, et al. Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs[J]. Journal of Neural Engineering, 2021, 18(4): 046076. doi: 10.1088/1741-2552/ac0489.
|
[49] |
HE Chao, LIU Jialu, ZHU Yuesheng, et al. Data augmentation for deep neural networks model in EEG classification task: A review[J]. Frontiers in Human Neuroscience, 2021, 15: 765525. doi: 10.3389/fnhum.2021.765525.
|
[50] |
PANWAR S, RAD P, QUARLES J, et al. Generating EEG signals of an RSVP experiment by a class conditioned Wasserstein generative adversarial network[C]. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019: 1304–1310. doi: 10.1109/SMC.2019.8914492.
|
[51] |
PANWAR S, RAD P, JUNG T P, et al. Modeling EEG data distribution with a Wasserstein generative adversarial network to predict RSVP events[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(8): 1720–1730. doi: 10.1109/TNSRE.2020.3006180.
|
[52] |
XU Meng, CHEN Yuanfang, WANG Yijun, et al. BWGAN-GP: An EEG data generation method for class imbalance problem in RSVP tasks[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 251–263. doi: 10.1109/TNSRE.2022.3145515.
|
[53] |
CUI Yujie, XIE Songyun, XIE Xinzhou, et al. Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection[J]. Frontiers in Computational Neuroscience, 2022, 16: 1006361. doi: 10.3389/fncom.2022.1006361.
|
[54] |
WU Qunjian, YAN Bin, ZENG Ying, et al. Anti-deception: Reliable EEG-based biometrics with real-time capability from the neural response of face rapid serial visual presentation[J]. BioMedical Engineering OnLine, 2018, 17(1): 55. doi: 10.1186/s12938-018-0483-7.
|
[55] |
ZENG Ying, WU Qunjian, YANG Kai, et al. EEG-based identity authentication framework using face rapid serial visual presentation with optimized channels[J]. Sensors (Basel), 2018, 19(1): 6. doi: 10.3390/s19010006.
|
[56] |
WANG Hanwen, QI Yu, YU Hang, et al. , RCIT: An RSVP-based concealed information test framework using EEG signals[J]. IEEE Transactions on Cognitive and Developmental Systems, 2022, 14(2): 541–551. doi: 10.1109/TCDS.2021.3053455.
|
[57] |
NAYAK T, KO L W, JUNG T P, et al. Target classification in a novel SSVEP-RSVP based BCI gaming system[C]. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019: 4194–4198. doi: 10.1109/SMC.2019.8914174.
|
[58] |
KO L W, SANKAR D S V, HUANG Yufei, et al. SSVEP-assisted RSVP brain–computer interface paradigm for multi-target classification[J]. Journal of Neural Engineering, 2021, 18(1): 016021. doi: 10.1088/1741-2552/abd1c0.
|
[59] |
ACKERMAN E and STRICKLAND E. Are you ready for workplace brain scanning?[EB/OL]. https://spectrum.ieee.org/neurotech-workplace-innereye-emotiv, 2022.
|
[60] |
YI Weibo, QIU Shuang, FAN Xinan, et al. Evaluation of mental workload associated with time pressure in rapid serial visual presentation tasks[J]. IEEE Transactions on Cognitive and Developmental Systems, 2022, 14(2): 608–616. doi: 10.1109/tcds.2021.3061564.
|
[61] |
TANG M F, FORD L, ARABZADEH E, et al. Neural dynamics of the attentional blink revealed by encoding orientation selectivity during rapid visual presentation[J]. Nature Communications, 2020, 11(1): 434. doi: 10.1038/s41467-019-14107-z.
|
[62] |
SUN Meng, LIU Fang, CUI Lixia, et al. The effect of fearful faces on the attentional blink is modulated by emotional task relevance: An event-related potential study[J]. Neuropsychologia, 2021, 162: 108043. doi: 10.1016/j.neuropsychologia.2021.108043.
|