非侵入式脑—机接口编解码技术研究进展

邱爽; 杨帮华; 陈小刚; 王毅军; 许敏鹏; 吕宝粮; 高小榕; 何晖光

doi:10.11834/jig.230031

智能交互与跨模态学习 | 浏览量 : 0 下载量: 7 CSCD: 0

PDF
导出
分享
收藏
专辑

非侵入式脑—机接口编解码技术研究进展
A survey on encoding and decoding technology of non-invasive brain-computer interface
2023年28卷第6期页码：1543-1566
纸质出版日期： 2023-06-16 ，
DOI： 10.11834/jig.230031
稿件说明：

移动端阅览

邱爽，杨帮华，陈小刚，王毅军，许敏鹏，吕宝粮，高小榕，何晖光. 2023. 非侵入式脑—机接口编解码技术研究进展. 中国图象图形学报， 28(06):1543-1566

Qiu Shuang， Yang Banghua， Chen Xiaogang， Wang Yijun， Xu Minpeng， Lyu Baoliang， Gao Xiaorong， He Huiguang. 2023. A survey on encoding and decoding technology of non-invasive brain-computer interface. Journal of Image and Graphics， 28(06):1543-1566
邱爽，杨帮华，陈小刚，王毅军，许敏鹏，吕宝粮，高小榕，何晖光. 2023. 非侵入式脑—机接口编解码技术研究进展. 中国图象图形学报， 28(06):1543-1566 DOI： 10.11834/jig.230031.

Qiu Shuang， Yang Banghua， Chen Xiaogang， Wang Yijun， Xu Minpeng， Lyu Baoliang， Gao Xiaorong， He Huiguang. 2023. A survey on encoding and decoding technology of non-invasive brain-computer interface. Journal of Image and Graphics， 28(06):1543-1566 DOI： 10.11834/jig.230031.

摘要

脑—机接口（brain-computer interface，BCI）系统通过采集、分析大脑信号，将其转换为输出指令，从而跨越外周神经系统，实现由大脑信号对外部设备的直接控制，进而用于替代、修复、增强、补充或改善中枢神经系统的正常输出。非侵入式脑—机接口由于具有安全性以及便携性等优点，得到了广泛关注和持续研究。研究人员对脑信号编码方法的不断探索扩展了BCI系统的应用场景和适用范围。同时，脑信号解码方法的不断研发极大地克服了脑电信号信噪比低的缺点，提高了系统性能，这都为构建高性能脑—机接口系统奠定了基础。本文综述了非侵入式脑—机接口编解码技术以及系统应用的最新研究进展，展望其未来发展前景，以期促进BCI系统的深入研究与广泛应用。

Abstract

A brain-computer interface （BCI） establishes a direct communication pathway between the living brain and an external device in terms of brain signals-acquiring and analysis and commands-converted output. It can be used to replace， repair， augment， supplement or improve the normal output of the central nervous system. The BCIs can be divided into invasive or noninvasive BCIs based on the placement of the acquisition electrode. An invasive BCI is linked to its records or brain neurons-relevant stimulation through surgical brain-implanted electrodes. But， it is just used for animal experiments or severe paralysis patients although invasive BCIs can be used to record a high signal-to-noise ratio-related brain signals. Non-invasive BCIs have its potentials of their credibility and portability in comparison with invasive BCIs. The electroencephalography （EEG） is commonly used for brain signal for BCIs now. The EEG-based BCI systems consist of two directions： coding methods used to generate brain signals and the decoding methods used to decode brain signals. In recent years， the growth of coding methods has extended the application scenarios and applicability of the system. Furthermore， to get high-performance BCIs， brain signal decoding methods has greatly developed and low signal-to-noise ratio of EEG signals is optimized as well. The BCI systems can be segmented into such categories of active， reactive or passive. For an active BCI， a user can consciously control mental activities of external stimuli excluded. The motor imagery based （MI-based） BCI system is an active BCI system in terms of EEG signals-within specific frequency changes， which can be balanced using non-motor output mental rehearsal of a motor action. For reactive BCI， brain activity is triggered by an external stimulus， and the user reacts to the stimulus from the external world. Most researches are focused on static-state visual evoked potential based （SSVEP-based） BCI and an event-related potential based （ERP-based） BCI. SSVEPs are brain responses that are elicited over the visual region when a user focuses on a flicker of a visual stimulus. An ERP-based BCI is usually based on a P300 component of ERP， which can be produced after the onset of the stimulus. For a passive BCI， it can provide the hidden state of the brain in the human-computer interaction process rather than temporal and humanized interaction， especially for affective BCIs and mental load BCIs. Our literature analysis is focused on BCI systems from four contexts of its MI-based， SSVEP-based， ERP-based， and affective BCI. Current situation and the application of BCI systems can be analyzed for coding and decoding technology as mentioned below： 1） MI-based BCI studies are mostly focused on the classification of EEG patterns during MI tasks from different limbs， such as the left hand， right hand， and both feet. However， small instruction sets are still challenged for the actual application requirements. Thus， fine MI tasks from the unilateral limb are developed to deal with that. We review fine MI paradigms of multiple unilateral limb contexts in relevance to its joints， directions， and tasks. Decoding methods consist of two procedures in common： feature extraction and feature classification. Feature extraction extracts task-related and recognizable features from brain signals， and feature classification uses features-extracted to clarify the intentions of users. For MI decoding technology， two-stage traditional decoding methods are first briefly introduced， e.g.， common spatial pattern （CSP） and linear discriminant analysis （LDA）. After that， we summarize the latest deep learning methods， which can improve the decoding accuracy， and some migration learning methods are summarized， which can alleviate its calibration data. Finally， recent applications of the MI-BCI system are introduced in related to control of the mechanical arm and stroke rehabilitation. 2） SSVEP-based BCI

systems are concerned about in terms of their high information transmission rate， strong stability， and applicability. Recent researches to highlight of SSVEP-based BCI studies are reviewed literately as well. For example， some new coding strategies are implemented for instruction set or users’ preference and such emerging decoding methods are used to optimize its performance of SSVEP detection. Additionally， we summarize the main application directions of SSVEP-based BCI systems， including communication， control， and state monitoring， and the latest application progress are reviewed as well. 3）

For ERP-based BCIs， the signal-to-noise ratio of the ERP response is required to be relatively higher， and a single target-related rapid serial visual presentation paradigm is challenged for the detection of multiple targets. To sum up， BCI-hybrid paradigms are developed in terms of the integration of the ERP and other related paradigms. Function-based ERP decoding methods are divided into four categories of signal de-noising， feature extraction， transfer learning， and zero calibration algorithms. Current status of decoding methods and ERP-based BCI applications are summarized as well. 4） Affective BCI can be adopted to recognize and balance human emotion. The emotion and new decoding methods are evolved in， including multimodal fusion and transfer learning. Additionally， the application of affective BCI in healthcare is reviewed and analyzed as well. Finally， future research direction of non-invasive BCI are predicted further.

关键词

脑—机接口（BCI）非侵入编码方法解码方法系统应用

Keywords

brain-computer interface（BCI）non-invasiveencoding methoddecoding methodsystem application

references

Abibullaev B， Kunanbayev K and Zollanvari A. 2022. Subject-independent classification of P300 event-related potentials using a small number of training subjects. IEEE Transactions on Human-Machine Systems， 52（5）： 843-854 ［DOI： 10.1109/THMS.2022.3189576http://dx.doi.org/10.1109/THMS.2022.3189576］

Adamczyk A K， Wyczesany M and van Peer J M. 2022. High working memory load impairs reappraisal but facilitates distraction —— An event-related potential investigation. Biological Psychology， 171： #108327 ［DOI： 10.1016/j.biopsycho.2022.108327http://dx.doi.org/10.1016/j.biopsycho.2022.108327］

Alghowinem S. 2013. From joyous to clinically depressed： mood detection using multimodal analysis of a person's appearance and speech//Proceedings of 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. Geneva， Switzerland： IEEE： 648-654 ［DOI： 10.1109/ACII.2013.113http://dx.doi.org/10.1109/ACII.2013.113］

Allison B Z， Wolpaw E W and Wolpaw J R. 2007. Brain-computer interface systems： progress and prospects. Expert Review of Medical Devices， 4（4）： 463-474 ［DOI： 10.1586/17434440.4.4.463http://dx.doi.org/10.1586/17434440.4.4.463］

Ang K K， Chin Z Y， Wang C C， Guan C T and Zhang H H. 2012. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2A and 2B. Frontiers in Neuroscience， 6： #39 ［DOI： 10.3389/fnins.2012.00039http://dx.doi.org/10.3389/fnins.2012.00039］

Ang K K， Chin Z Y， Zhang H H and Guan C T. 2008. Filter bank common spatial pattern （FBCSP） in brain-computer interface//Proceedings of 2008 IEEE International Joint Conference on Neural Networks （IEEE World Congress on Computational Intelligence）. Hong Kong， China： IEEE： 2390-2397 ［DOI： 10.1109/IJCNN.2008.4634130http://dx.doi.org/10.1109/IJCNN.2008.4634130］

Anumanchipalli G K， Chartier J and Chang E F. 2019. Speech synthesis from neural decoding of spoken sentences. Nature， 568（7753）： 493-498 ［DOI： 10.1038/s41586-019-1119-1http://dx.doi.org/10.1038/s41586-019-1119-1］

Bai L， Ma H， Huang Y X and Luo Y J. 2005. The development of native Chinese affective picture system-A pretest in 46 college students. Chinese Mental Health Journal， 19（11）： 719-722

白露，马慧，黄宇霞，罗跃嘉. 2005. 中国情绪图片系统的编制——在46名中国大学生中的试用. 中国心理卫生杂志， 19（11）： 719-722 ［DOI： 10.3321/j.issn：1000-6729.2005.11.001http://dx.doi.org/10.3321/j.issn：1000-6729.2005.11.001］

Barachant A， Bonnet S， Congedo M and Jutten C. 2012. Multiclass brain-computer interface classification by riemannian geometry. IEEE Transactions on Biomedical Engineering， 59（4）： 920-928 ［DOI： 10.1109/tbme.2011.2172210http://dx.doi.org/10.1109/tbme.2011.2172210］

Bermudez i Badia S， Morgade A G， Samaha H and Verschure P F M J. 2013. Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 21（2）： 174-181 ［DOI： 10.1109/TNSRE.2012.2229295http://dx.doi.org/10.1109/TNSRE.2012.2229295］

Bethge D， Hallgarten P， Grosse-Puppendahl T， Kari M， Chuang L L， Özdenuzci O and Schmidt A. 2022. EEG2Vec： learning affective EEG representations via Variational Autoencoders. ［EB/OL］. ［2023-01-01］. https://arxiv.org/pdf/2207.08002.pdfhttps://arxiv.org/pdf/2207.08002.pdf

Bin G Y， Gao X R， Wang Y J， Li Y， Hong B and Gao S K. 2011. A high-speed BCI based on code modulation VEP. Journal of Neural Engineering， 8（2）： #025015 ［DOI： 10.1088/1741-2560/8/2/025015http://dx.doi.org/10.1088/1741-2560/8/2/025015］

Bokk O and Forster B. 2022. The effect of a short mindfulness meditation on somatosensory attention. Mindfulness， 13（8）： 2022-2030 ［DOI： 10.1007/s12671-022-01938-zhttp://dx.doi.org/10.1007/s12671-022-01938-z］

Cai X G and Pan J H. 2022. Toward a brain-computer interface- and internet of things-based smart ward collaborative system using hybrid signals. Journal of Healthcare Engineering， 2022： #6894392 ［DOI： 10.1155/2022/6894392http://dx.doi.org/10.1155/2022/6894392］

Cao H T， Zhong Z P， Chen Y F， Mei J， Li A， Xu M P and Ming D. 2022. Research advances in non-invasive brain-computer interface control strategies. Journal of Biomedical Engineering， 39（5）： 1033-1040

曹洪涛，钟子平，陈远方，梅杰，李昂，许敏鹏，明东. 2022. 非侵入式脑机接口控制策略的研究进展. 生物医学工程学杂志， 39（5）： 1033-1040 ［DOI： 10.7507/1001-5515.202205013http://dx.doi.org/10.7507/1001-5515.202205013］

Cao L F， Li G Y， Xu Y， Zhang H， Shu X K and Zhang D G. 2021. A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy. Journal of Neural Engineering， 18（4）： #046045 ［DOI： 10.1088/1741-2552/abf8cbhttp://dx.doi.org/10.1088/1741-2552/abf8cb］

Caria A， Da Rocha J L D， Gallitto G， Birbaumer N， Sitaram R and Murguialday A R. 2020. Brain-machine interface induced morpho-functional remodeling of the neural motor system in severe chronic stroke. Neurotherapeutics， 17（2）： 635-650 ［DOI： 10.1007/s13311-019-00816-2http://dx.doi.org/10.1007/s13311-019-00816-2］

Chang W W， Wang H， Lu Z G and Liu C. 2020. A concealed information test system based on functional brain connectivity and signal entropy of audio-visual ERP. IEEE Transactions on Cognitive and Developmental Systems， 12（2）： 361-370 ［DOI： 10.1109/TCDS.2020.2991359http://dx.doi.org/10.1109/TCDS.2020.2991359］

Chen J B， Zhang Y S， Pan Y D， Xu P and Guan C. 2022a. A transformer-based deep neural network model for SSVEP classification ［EB/OL］. ［2023-01-01］. https://arxiv.org/pdf/2210.04172.pdfhttps://arxiv.org/pdf/2210.04172.pdf

Chen J F， Chen Y and Wang B. 2022b. Cross-subject domain adaptation for multi-frame EEG images ［EB/OL］. ［2023-01-01］. https://arxiv.org/pdf/2106.06769.pdfhttps://arxiv.org/pdf/2106.06769.pdf

Chen J J， Wang Y J， Maye A， Hong B， Gao X R， Engel A K and Zhang D. 2021a. A spatially-coded visual brain-computer interface for flexible visual spatial information decoding. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 29： 926-933 ［DOI： 10.1109/TNSRE.2021.3080045http://dx.doi.org/10.1109/TNSRE.2021.3080045］

Chen L L， Chen P F， Zhao S K， Luo Z G， Chen W， Pei Y， Zhao H Y， Jiang J， Xu M P， Yan Y and Yin E W. 2021b. Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain-computer interface. Journal of Neural Engineering， 18（6）： #066005 ［DOI： 10.1088/1741-2552/ac3044http://dx.doi.org/10.1088/1741-2552/ac3044］

Chen P Y， Gao Z K， Yin M M， Wu J L， Ma K and Grebogi C. 2022c. Multiattention adaptation network for motor imagery recognition. IEEE Transactions on Systems， Man， and Cybernetics， 52（8）： Systems， 52（8）： 5127-5139 ［DOI： 10.1109/TSMC.2021.3114145http://dx.doi.org/10.1109/TSMC.2021.3114145］

Chen X， Yu Y， Tang J S， Zhou L， Liu K X， Liu Z Y， Chen S M， Wang J， Zeng L L， Liu J F and Hu D W. 2022e. Clinical validation of BCI-vontrolled wheelchairs in subjects with severe spinal cord injury. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 30： 579-589 ［DOI： 10.1109/TNSRE.2022.3156661http://dx.doi.org/10.1109/TNSRE.2022.3156661］

Chen X G， Chen Z K， Gao S K and Gao X R. 2014. A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces， 1（3/4）： 181-191 ［DOI： 10.1080/2326263X.2014.944469http://dx.doi.org/10.1080/2326263X.2014.944469］

Chen X G， Huang X S， Wang Y J and Gao X R. 2020. Combination of augmented reality based brain-computer interface and computer vision for high-level control of a robotic arm. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 28（12）： 3140-3147 ［DOI： 10.1109/TNSRE.2020.3038209http://dx.doi.org/10.1109/TNSRE.2020.3038209］

Chen X G， Liu B C， Wang Y J and Gao X R. 2022d. A spectrally-dense encoding method for designing a high-speed SSVEP-BCI with 120 stimuli. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 30： 2764-2772 ［DOI： 10.1109/TNSRE.2022.3208717http://dx.doi.org/10.1109/TNSRE.2022.3208717］

Chen X G， Wang Y J， Nakanishi M， Gao X R， Jung T P and Gao S K. 2015. High-speed spelling with a noninvasive brain-computer interface. Proceedings of the National Academy of Sciences of the United States of America， 112（44）： E6058-E6067 ［DOI： 10.1073/pnas.1508080112http://dx.doi.org/10.1073/pnas.1508080112］

Chen Y， Yang R， Huang M J， Wang Z D and Liu X H. 2022f. Single-source to single-target cross-subject motor imagery classification based on multisubdomain adaptation network. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 30： 1992-2002 ［DOI： 10.1109/TNSRE.2022.3191869http://dx.doi.org/10.1109/TNSRE.2022.3191869］

Chen Y H， Yang C， Ye X C， Chen X G， Wang Y J and Gao X R. 2021c. Implementing a calibration-free SSVEP-based BCI system with 160 targets. Journal of Neural Engineering， 18（4）： #046094 ［DOI： 10.1088/1741-2552/ac0bfahttp://dx.doi.org/10.1088/1741-2552/ac0bfa］

Cheng M and Gao S K. 1999. An EEG-based cursor control system//Proceedings of the 1st Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology the 21st Annual Conference and 1999 Annual Fall Meeting of the Biomedical Engineering Society. Atlanta， USA： IEEE： #669 ［DOI： 10.1109/IEMBS.1999.802747http://dx.doi.org/10.1109/IEMBS.1999.802747］

Cheng M， Gao X R， Gao S K and Xu D F. 2002. Design and implementation of a brain-computer interface with high transfer rates. IEEE Transactions on Biomedical Engineering， 49（10）： 1181-1186 ［DOI： 10.1109/TBME.2002.803536http://dx.doi.org/10.1109/TBME.2002.803536］

Chi X Y， Cui H Y and Chen X G. 2022. Advances in multi-modal brain-computer interface combined with steady-state visual evoked potential. Chinese Journal of Biomedical Engineering， 41（2）： 204-213

迟新一，崔红岩，陈小刚. 2022. 结合稳态视觉诱发电位的多模态脑机接口研究进展. 中国生物医学工程学报， 41（2）： 204-213 ［DOI： 10.3969/j.issn.0258-8021.2022.02.009http://dx.doi.org/10.3969/j.issn.0258-8021.2022.02.009］

Chien Y Y， Lin F C， Zao J K， Chou C C， Huang Y P， Kuo H Y， Wang Y J， Jung T P and Shieh H P D. 2017. Polychromatic SSVEP stimuli with subtle flickering adapted to brain-display interactions. Journal of Neural Engineering， 14（1）： #016018 ［DOI： 10.1088/1741-2552/aa550dhttp://dx.doi.org/10.1088/1741-2552/aa550d］

Cimtay Y and Ekmekcioglu E. 2020. Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition. Sensors， 20（7）： #2034 ［DOI： 10.3390/s20072034http://dx.doi.org/10.3390/s20072034］

Coyle D， Garcia J， Satti A R and McGinnity T M. 2011. EEG-based continuous control of a game using a 3 channel motor imagery BCI： BCI game//Proceedings of 2011 IEEE Symposium on Computational Intelligence， Cognitive Algorithms， Mind， and Brain （CCMB）. Paris， France： IEEE： 1-7 ［DOI： 10.1109/CCMB.2011.5952128http://dx.doi.org/10.1109/CCMB.2011.5952128］

Cui Y J， Xie S Y， Xie X Z， Duan X and Gao C L. 2022. A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals. Journal of Biomedical Engineering， 39（1）： 39-46

崔玉洁，谢松云，谢辛舟，段绪，高川林. 2022. 针对快速序列视觉呈现脑电信号的时空混合特征提取方法. 生物医学工程学杂志， 39（1）： 39-46 ［DOI： 10.7507/1001-5515.202104049http://dx.doi.org/10.7507/1001-5515.202104049］

Cygan H B， Nowicka M M and Nowicka A. 2022. Impaired attentional bias toward one’s own face in autism spectrum disorder： ERP evidence. Autism Research， 15（2）： 241-253 ［DOI： 10.1002/aur.2647http://dx.doi.org/10.1002/aur.2647］

Défossez A， Caucheteux C， Rapin J， Kabeli O and King J R. 2022. Decoding speech from non-invasive brain recordings ［EB/OL］. ［2023-01-01］. https://arxiv.org/pdf/2208.12266v1.pdfhttps://arxiv.org/pdf/2208.12266v1.pdf

Ding W L， Shan J H， Fang B， Wang C Y， Sun F C and Li X Y. 2021. Filter bank convolutional neural network for short time-window steady-state visual evoked potential classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 29： 2615-2624 ［DOI： 10.1109/TNSRE.2021.3132162http://dx.doi.org/10.1109/TNSRE.2021.3132162］

Donchin E and Lindsley D B. 1969. Average evoked potentials： methods， results， and evaluations. U.S. Government Printing Office ［DOI： 10.1037/13016-000http://dx.doi.org/10.1037/13016-000］

Dzirasa K， Fuentes R， Kumar S， Potes J M and Nicolelis M A L. 2011a. Chronic in vivo multi-circuit neurophysiological recordings in mice. Journal of Neuroscience Methods， 195（1）： 36-46 ［DOI： 10.1016/j.jneumeth.2010.11.014http://dx.doi.org/10.1016/j.jneumeth.2010.11.014］

Dzirasa K， McGarity D L， Bhattacharya A， Kumar S， Takahashi J S， Dunson D， McClung C A and Nicolelis M A L. 2011b. Impaired limbic gamma oscillatory synchrony during anxiety-related behavior in a genetic mouse model of bipolar mania. The Journal of Neuroscience， 31（17）： 6449-6456 ［DOI： 10.1523/JNEUROSCI.6144-10.2011http://dx.doi.org/10.1523/JNEUROSCI.6144-10.2011］

Edelman B J， Baxter B and He B. 2016. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Transactions on Biomedical Engineering， 63（1）： 4-14 ［DOI： 10.1109/TBME.2015.2467312http://dx.doi.org/10.1109/TBME.2015.2467312］

Edelman B J， Meng J， Suma D， Zurn C， Nagarajan E， Baxter B S， Cline C C and He B. 2019. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Science Robotics， 4（31）： #6844 ［DOI： 10.1126/scirobotics.aaw6844http://dx.doi.org/10.1126/scirobotics.aaw6844］

Eldeeb S， Susam B T， Akcakaya M， Conner C M， White S W and Mazefsky C A. 2021. Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD. Scientific Reports， 11（1）： #6000 ［DOI： 10.1038/s41598-021-85362-8http://dx.doi.org/10.1038/s41598-021-85362-8］

Fan K F， Dong J， Yu Y T， Lin F， Pan G， Wang Y M， Xu J， Pu J B， Li Y Q， Pan J H， Yao D Z， Qin Y， Qian T Y， Li J H， Huang C， Ma W Z， Bruce P， Li T and Zeng Y. 2021. Brain-computer interface standardization white paper （2021）. Beijing： China Electronics Standardization Institute

范科峰，董建，余云涛，蔺芳，潘纲，王跃明，徐建，蒲江波，李远清，潘家辉，尧德中，秦云，钱天翼，李建慧，黄超，马万钟， Bruce P，李婷，曾毅. 2021. 脑机接口标准化白皮书（2021版）. 北京：中国电子技术标准化研究院

Gao X R， Wang Y J， Chen X G and Gao S K. 2021. Interface， interaction， and intelligence in generalized brain-computer interfaces. Trends in Cognitive Sciences， 25（8）： 671-684 ［DOI： 10.1016/j.tics.2021.04.003http://dx.doi.org/10.1016/j.tics.2021.04.003］

Gao X R， Xu D F， Cheng M and Gao S K. 2003. A BCI-based environmental controller for the motion-disabled. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 11（2）： 137-140 ［DOI： 10.1109/TNSRE.2003.814449http://dx.doi.org/10.1109/TNSRE.2003.814449］

Gehrke L， Lopes P， Klug M， Akman S and Gramann K. 2022. Neural sources of prediction errors detect unrealistic VR interactions. Journal of Neural Engineering， 19（3）： #036002 ［DOI： 10.1088/1741-2552/ac69bchttp://dx.doi.org/10.1088/1741-2552/ac69bc］

Gergondet P， Druon S， Kheddar A， Hintermüller C， Guger C and Slater M. 2011. Using brain-computer interface to steer a humanoid robot//Proceedings of 2011 IEEE International Conference on Robotics and Biomimetics. Karon Beach， Thailand： IEEE： 192-197 ［DOI： 10.1109/ROBIO.2011.6181284http://dx.doi.org/10.1109/ROBIO.2011.6181284］

Gong X， Huang Y X， Wang Y and Luo Y J. 2011. Revision of the Chinese facial affective picture system. Chinese Mental Health Journal， 25（1）： 40-46

龚栩，黄宇霞，王妍，罗跃嘉. 2011. 中国面孔表情图片系统的修订. 中国心理卫生杂志， 25（1）： 40-46 ［DOI： 10.3969/j.issn.1000-6729.2011.01.011http://dx.doi.org/10.3969/j.issn.1000-6729.2011.01.011］

Guney O B， Oblokulov M and Ozkan H. 2022. A deep neural network for SSVEP-based brain-computer interfaces. IEEE Transactions on Biomedical Engineering， 69（2）： 932-944 ［DOI： 10.1109/TBME.2021.3110440http://dx.doi.org/10.1109/TBME.2021.3110440］

Guo W T， Yang H W， Liu Z Y， Xu Y P and Hu B. 2021. Deep neural networks for depression recognition based on 2D and 3D facial expressions under emotional stimulus tasks. Frontiers in Neuroscience， 15： #609760 ［DOI： 10.3389/fnins.2021.609760http://dx.doi.org/10.3389/fnins.2021.609760］

Habelt B， Wirth C， Afanasenkau D， Mihaylova L， Winter C， Arvaneh M， Minev I R and Bernhardt N. 2021. A multimodal neuroprosthetic interface to record， modulate and classify electrophysiological biomarkers relevant to neuropsychiatric disorders. Frontiers in Bioengineering and Biotechnology， 9： #770274 ［DOI： 10.3389/fbioe.2021.770274http://dx.doi.org/10.3389/fbioe.2021.770274］

Hasan S M S， Marquez J S， Siddiquee M R， Fei D Y and Bai O. 2021. Preliminary study on real-time prediction of gait acceleration intention from volition-associated EEG patterns. IEEE Access， 9： 62676-62686 ［DOI： 10.1109/ACCESS.2021.3075253http://dx.doi.org/10.1109/ACCESS.2021.3075253］

He F， Dong B W， Han J， Li Y L， Xu M P and Ming D. 2022. Advances in application of game brain-computer interface based on electroencephalogram. Journal of Electronics and Information Technology， 44（2）： 415-423

何峰，董博文，韩锦，李雨龙，许敏鹏，明东. 2022. 基于头皮脑电的游戏型脑机接口应用研究综述. 电子与信息学报， 44（2）： 415-423 ［DOI： 10.11999/JEIT211337http://dx.doi.org/10.11999/JEIT211337］

Hong X L， Zheng Q Q， Liu L Y， Chen P Y， Ma K， Gao Z K and Zheng Y F. 2021. Dynamic joint domain adaptation network for motor imagery classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 29： 556-565 ［DOI： 10.1109/TNSRE.2021.3059166http://dx.doi.org/10.1109/TNSRE.2021.3059166］

Hu X， Wang F and Zhang D. 2022. Similar brains blend emotion in similar ways： neural representations of individual difference in emotion profiles. NeuroImage， 247： #118819 ［DOI： 10.1016/j.neuroimage.2021.118819http://dx.doi.org/10.1016/j.neuroimage.2021.118819］

Hwang H J， Hwan Kim D， Han C H and Im C H. 2013. A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface （BCI）. Brain Research， 1515： 66-77 ［DOI： 10.1016/j.brainres.2013.03.050http://dx.doi.org/10.1016/j.brainres.2013.03.050］

Jeong J H， Cho J H， Shim K H， Kwon B H， Lee B H， Lee D Y， Lee D H and Lee S W. 2020a. Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions. GigaScience， 9（10）： #giaa098 ［DOI： 10.1093/gigascience/giaa098http://dx.doi.org/10.1093/gigascience/giaa098］

Jeong J H， Shim K H， Kim D J and Lee S W. 2020b. Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 28（5）： 1226-1238 ［DOI： 10.1109/TNSRE.2020.2981659http://dx.doi.org/10.1109/TNSRE.2020.2981659］

Ji L L， Zhao Q， Zhang Y F， Wan J J， Yu Y F， Zhao J F and Li X M. 2022. Event-related brain potential correlates of event-based prospective memory in children with learning disability. Frontiers in Psychiatry， 13： #898536 ［DOI： 10.3389/fpsyt.2022.898536http://dx.doi.org/10.3389/fpsyt.2022.898536］

Jiang H F， Guan X Y， Zhao W Y， Zhao L M and Lu B L. 2019. Generating multimodal features for emotion classification from eye movement signals. Australian Journal of Intelligent Information Processing Systems， 15（3）： 59-66

Jiang L， Li X Y， Pei W H， Gao X R and Wang Y J. 2022a. A hybrid brain-computer interface based on visual evoked potential and pupillary response. Frontiers in Human Neuroscience， 16： #834959 ［DOI： 10.3389/fnhum.2022.834959http://dx.doi.org/10.3389/fnhum.2022.834959］

Jiang L， Pei W H and Wang Y J. 2022b. A user-friendly SSVEP-based BCI using imperceptible phase-coded flickers at 60 Hz. China Communications， 19（2）： 1-14 ［DOI： 10.23919/JCC.2022.02.001http://dx.doi.org/10.23919/JCC.2022.02.001］

Joseph A B. 1985. Design considerations for the brain-machine interface. Medical Hypotheses， 17（3）： 191-195 ［DOI： 10.1016/0306-9877（85）90124-0http://dx.doi.org/10.1016/0306-9877（85）90124-0］

Katsigiannis S and Ramzan N. 2018. DREAMER： a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE Journal of Biomedical and Health Informatics， 22（1）： 98-107 ［DOI： 10.1109/JBHI.2017.2688239http://dx.doi.org/10.1109/JBHI.2017.2688239］

Khan M A， Das R， Iversen H K and Puthusserypady S. 2020. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation： from designing to application. Computers in Biology and Medicine， 123： #103843 ［DOI： 10.1016/j.compbiomed.2020.103843http://dx.doi.org/10.1016/j.compbiomed.2020.103843］

Kim S， Lee S， Kang H， Kim S and Ahn M. 2021. P300 brain-computer interface-based drone control in virtual and augmented reality. Sensors， 21（17）： #5765 ［DOI： 10.3390/s21175765http://dx.doi.org/10.3390/s21175765］

Klawohn J， Brush C J and Hajcak G. 2021. Neural responses to reward and pleasant pictures prospectively predict remission from depression. Journal of Abnormal Psychology， 130（7）： 702-712 ［DOI： 10.1037/abn0000696http://dx.doi.org/10.1037/abn0000696］

Ko L W， Sankar D S V， Huang Y F， Lu Y C， Shaw S and Jung T P. 2021. SSVEP-assisted RSVP brain-computer interface paradigm for multi-target classification. Journal of Neural Engineering， 18（1）： #016021 ［DOI： 10.1088/1741-2552/abd1c0http://dx.doi.org/10.1088/1741-2552/abd1c0］

Koelstra S， Muhl C， Soleymani M， Lee J S， Yazdani A， Ebrahimi T， Pun T， Nijholt A and Patras I. 2012. DEAP： a database for emotion analysis； using physiological signals. IEEE Transactions on Affective Computing， 3（1）： 18-31 ［DOI： 10.1109/T-AFFC.2011.15http://dx.doi.org/10.1109/T-AFFC.2011.15］

Kool L， Oranje B， Meijs H， De Wilde B， van Hecke J， Niemegeers P and Luykx J J. 2022. Event-related potentials and use of psychotropic medication in major psychiatric disorders. Psychiatry Research， 314： #114637 ［DOI： 10.1016/j.psychres.2022.114637http://dx.doi.org/10.1016/j.psychres.2022.114637］

Korkmaz O E， Aydemir O， Oral E A and Ozbek I Y. 2022. An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation. PLoS ONE， 17（4）： #0265904 ［DOI： 10.1371/journal.pone.0265904http://dx.doi.org/10.1371/journal.pone.0265904］

Kwak N S， Müller K R and Lee S W. 2015. A lower limb exoskeleton control system based on steady state visual evoked potentials. Journal of Neural Engineering， 12（5）： #056009 ［DOI： 10.1088/1741-2560/12/5/056009http://dx.doi.org/10.1088/1741-2560/12/5/056009］

Lang P J， Bradley M M and Cuthbert B N. 1997. International affective picture system （IAPS）： technical manual and affective ratings. NIMH Center for the Study of Emotion and Attention， Gainesville： 39-58

Lashgari E， Liang D H and Maoz U. 2020. Data augmentation for deep-learning-based electroencephalography. Journal of Neuroscience Methods， 346： #108885 ［DOI： 10.1016/j.jneumeth.2020.108885http://dx.doi.org/10.1016/j.jneumeth.2020.108885］

Lawhern V J， Solon A J， Waytowich N R， Gordon S M， Hung C P and Lance B J. 2018. EEGNet： a compact convolutional neural network for EEG-based brain-computer interfaces. Journal of Neural Engineering， 15（5）： #056013 ［DOI： 10.1088/1741-2552/aace8chttp://dx.doi.org/10.1088/1741-2552/aace8c］

Layer N， Weglage A， Müller V， Meister H， Lang-Roth R， Walger M， Murray M M and Sandmann P. 2022. The timecourse of multisensory speech processing in unilaterally stimulated cochlear implant users revealed by ERPs. NeuroImage： Clinical， 34： #102982 ［DOI： 10.1016/j.nicl.2022.102982http://dx.doi.org/10.1016/j.nicl.2022.102982］

Lee J， Won K， Kwon M， Jun S C and Ahn M. 2020. CNN with large data achieves true zero-training in online P300 brain-computer interface. IEEE Access， 8： 74385-74400 ［DOI： 10.1109/ACCESS.2020.2988057http://dx.doi.org/10.1109/ACCESS.2020.2988057］

Lee Y C， Lin W C， Cherng F Y and Ko L W. 2016. A visual attention monitor based on steady-state visual evoked potential. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 24（3）： 399-408 ［DOI： 10.1109/TNSRE.2015.2501378http://dx.doi.org/10.1109/TNSRE.2015.2501378］

Lees S， McCullagh P， Payne P， Maguire L， Lotte F and Coyle D. 2020. Speed of rapid serial visual presentation of pictures， numbers and words affects event-related potential-based detection accuracy. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 28（1）： 113-122 ［DOI： 10.1109/TNSRE.2019.2953975http://dx.doi.org/10.1109/TNSRE.2019.2953975］

Li B W， Lin Y F， Gao X R and Liu Z W. 2021a. Enhancing the EEG classification in RSVP task by combining interval model of ERPs with spatial and temporal regions of interest. Journal of Neural Engineering， 18（1）： #016008 ［DOI： 10.1088/1741-2552/abc8d5http://dx.doi.org/10.1088/1741-2552/abc8d5］

Li C， Zhang Z Z， Zhang X D， Huang G N， Liu Y and Chen X. 2022a. EEG-based emotion recognition via transformer neural architecture search. IEEE Transactions on Industrial Informatics， 19（4）：6016-6025 ［DOI： 10.1109/TII.2022.3170422http://dx.doi.org/10.1109/TII.2022.3170422］

Li H， Jin Y M， Zheng W L and Lu B L. 2018. Cross-subject emotion recognition using deep adaptation networks//Proceedings of the 25th International Conference on Neural Information Processing. Siem Reap， Cambodia： Springer： 403-413 ［DOI： 10.1007/978-3-030-04221-9_36http://dx.doi.org/10.1007/978-3-030-04221-9_36］

Li J P， Qiu S， Shen Y Y， Liu C L and He H G. 2020a. Multisource transfer learning for cross-subject EEG emotion recognition. IEEE Transactions on Cybernetics， 50（7）： 3281-3293 ［DOI： 10.1109/TCYB.2019.2904052http://dx.doi.org/10.1109/TCYB.2019.2904052］

Li M F， Wu L Y， Xu G Z， Duan F and Zhu C. 2022b. A robust 3D-convolutional neural network-based electroencephalogram decoding model for the intra-individual difference. International Journal of Neural Systems， 32（7）： #2250034 ［DOI： 10.1142/S0129065722500344http://dx.doi.org/10.1142/S0129065722500344］

Li W Y， Wu C P， Hu X， Chen J J， Fu S M， Wang F and Zhang D. 2022c. Quantitative personality predictions from a brief EEG recording. IEEE Transactions on Affective Computing， 13（3）： 1514-1527 ［DOI： 10.1109/TAFFC.2020.3008775http://dx.doi.org/10.1109/TAFFC.2020.3008775］

Li X J， Wei W， Qiu S and He H G. 2022d. TFF-former： temporal-frequency fusion transformer for zero-training decoding of two BCI tasks//Proceedings of the 30th ACM International Conference on Multimedia. Lisbon， Portugal： ACM： 51-59 ［DOI： 10.1145/3503161.3548269http://dx.doi.org/10.1145/3503161.3548269］

Li Y， Guo L H， Liu Y， Liu J Y and Meng F G. 2021b. A temporal-spectral-based squeeze-and-excitation feature fusion network for motor imagery EEG decoding. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 29： 1534-1545 ［DOI： 10.1109/TNSRE.2021.3099908http://dx.doi.org/10.1109/TNSRE.2021.3099908］

Li Y， Xiang J Y and Kesavadas T. 2020b. Convolutional correlation analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 28（12）： 2681-2690 ［DOI： 10.1109/TNSRE.2020.3038718http://dx.doi.org/10.1109/TNSRE.2020.3038718］

Li Y， Zheng W M， Wang L， Zong Y and Cui Z. 2022e. From regional to global brain： a novel hierarchical spatial-temporal neural network model for EEG emotion recognition. IEEE Transactions on Affective Computing， 13（2）： 568-578 ［DOI： 10.1109/TAFFC.2019.2922912http://dx.doi.org/10.1109/TAFFC.2019.2922912］

Li Y L， Shen H and Hu D W. 2023. A spiking neural network for brain-computer interface of four classes motor imagery//3rd International Workshop on Human Brain and Artificial Intelligence. Vienna， Austria： Springer： 148-160 ［DOI： 10.1007/978-981-19-8222-4_13http://dx.doi.org/10.1007/978-981-19-8222-4_13］

Liang L Y， Bin G Y， Chen X G， Wang Y J， Gao S K and Gao X R. 2021. Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear. Journal of Neural Engineering， 18（6）： #066040 ［DOI： 10.1088/1741-2552/ac40a1http://dx.doi.org/10.1088/1741-2552/ac40a1］

Lin Y P and Jung T P. 2017. Improving EEG-based emotion classification using conditional transfer learning. Frontiers in Human Neuroscience， 11： #334 ［DOI： 10.3389/fnhum.2017.00334http://dx.doi.org/10.3389/fnhum.2017.00334］

Liu B C， Chen X G， Li X， Wang Y J， Gao X R and Gao S K. 2022a. Align and pool for EEG headset domain adaptation （ALPHA） to facilitate dry electrode based SSVEP-BCI. IEEE Transactions on Biomedical Engineering， 69（2）： 795-806 ［DOI： 10.1109/TBME.2021.3105331http://dx.doi.org/10.1109/TBME.2021.3105331］

Liu B C， Chen X G， Shi N L， Wang Y J， Gao S K and Gao X R. 2021a. Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 29： 1998-2007 ［DOI： 10.1109/TNSRE.2021.3114340http://dx.doi.org/10.1109/TNSRE.2021.3114340］

Liu C， Jin J， Daly I， Li S R， Sun H， Huang Y T， Wang X Y and Cichocki A. 2022b. SincNet-based hybrid neural network for motor imagery EEG decoding. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 30： 540-549 ［DOI： 10.1109/TNSRE.2022.3156076http://dx.doi.org/10.1109/TNSRE.2022.3156076］

Liu W， Qiu J L， Zheng W L and Lu B L. 2022c. Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition. IEEE Transactions on Cognitive and Developmental Systems， 14（2）： 715-729 ［DOI： 10.1109/TCDS.2021.3071170http://dx.doi.org/10.1109/TCDS.2021.3071170］

Liu W， Zheng W L， Li Z Y， Wu S Y， Gan L and Lu B L. 2022d. Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese， German， and French people. Journal of Neural Engineering， 19（2）： #026012 ［DOI： 10.1088/1741-2552/ac5c8dhttp://dx.doi.org/10.1088/1741-2552/ac5c8d］

Liu X B， Liu B C， Dong G Y， Gao X R and Wang Y J. 2022e. Facilitating applications of SSVEP-based BCIs by within-subject information transfer. Frontiers in Neuroscience， 16： #863359 ［DOI： 10.3389/fnins.2022.863359http://dx.doi.org/10.3389/fnins.2022.863359］

Liu X L， Lv L Y， Shen Y L， Xiong P， Yang J L and Liu J. 2021b. Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification. Journal of Neural Engineering， 18（2）： #026003 ［DOI： 10.1088/1741-2552/abd82bhttp://dx.doi.org/10.1088/1741-2552/abd82b］

Liu Y M， Höllerer T and Sra M. 2022f. SRI-EEG： state-based recurrent imputation for EEG artifact correction. Frontiers in Computational Neuroscience， 16： #803384 ［DOI： 10.3389/fncom.2022.803384http://dx.doi.org/10.3389/fncom.2022.803384］

Lotze M and Halsband U. 2006. Motor imagery. Journal of Physiology-Paris， 99（4/6）： 386-395 ［DOI： 10.1016/j.jphysparis.2006.03.012http://dx.doi.org/10.1016/j.jphysparis.2006.03.012］

Luo J， Wang Y J， Liu G M， Wang X F， Lu X F and Hei X H. 2021. Mirror convolutional neural network for motor imagery electroencephalogram recognition. Journal of Image and Graphics， 26（9）： 2257-2269

罗靖，王耀杰，刘光明，王晓帆，鲁晓锋，黑新宏. 2021. 面向运动想象脑电图识别的镜卷积神经网络. 中国图象图形学报， 26（9）： 2257-2269 ［DOI： 10.11834/jig.210072http://dx.doi.org/10.11834/jig.210072］

Luo S， Lan Y T， Peng D， Li Z Y， Zheng W L and Lu B L. 2022. Multimodal emotion recognition in response to oil paintings//Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society （EMBC）. Glasgow， UK： IEEE： 4167-4170 ［DOI： 10.1109/EMBC48229.2022.9871630http://dx.doi.org/10.1109/EMBC48229.2022.9871630］

Luo Y， Zhang S Y， Zheng W L and Lu B L. 2018. WGAN domain adaptation for EEG-based emotion recognition//Processings of the 25th International Conference on Neural Information Processing. Siem Reap， Cambodia： Springer： 275-286 ［DOI： 10.1007/978-3-030-04221-9_25http://dx.doi.org/10.1007/978-3-030-04221-9_25］

Luo Y， Zhu L Z， Wan Z Y and Lu B L. 2020. Data augmentation for enhancing EEG-based emotion recognition with deep generative models. Journal of Neural Engineering， 17（5）： #056021 ［DOI： 10.1088/1741-2552/abb580http://dx.doi.org/10.1088/1741-2552/abb580］

Ma B Q， Li H， Zheng W L and Lu B L. 2019. Reducing the subject variability of EEG signals with adversarial domain generalization//Proceedings of the 26th International Conference on Neural Information Processing. Sydney， Australia： Springer： 30-42 ［DOI： 10.1007/978-3-030-36708-4_3http://dx.doi.org/10.1007/978-3-030-36708-4_3］

Ma X L， Qiu S and He H G. 2020. Multi-channel EEG recording during motor imagery of different joints from the same limb. Scientific Data， 7（1）： #191 ［DOI： 10.1038/s41597-020-0535-2http://dx.doi.org/10.1038/s41597-020-0535-2］

Ma X L， Qiu S and He H G. 2022. Time-distributed attention network for EEG-based motor imagery decoding from the same limb. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 30： 496-508 ［DOI： 10.1109/TNSRE.2022.3154369http://dx.doi.org/10.1109/TNSRE.2022.3154369］

Mane R， Robinson N， Vinod A P， Lee S W and Guan C T. 2020. A multi-view CNN with novel variance layer for motor imagery brain computer interface//Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society （EMBC）. Montreal， Canada： IEEE： 2950-2953 ［DOI： 10.1109/EMBC44109.2020.9175874http://dx.doi.org/10.1109/EMBC44109.2020.9175874］

Marinou A， Saunders R and Casson A J. 2020. Flexible inkjet printed sensors for behind-the-ear SSVEP EEG monitoring//Proceedings of 2020 IEEE International Conference on Flexible and Printable Sensors and Systems （FLEPS）. Manchester， UK： IEEE： 1-4 ［DOI： 10.1109/FLEPS49123.2020.9239488http://dx.doi.org/10.1109/FLEPS49123.2020.9239488］

McFarland D J and Wolpaw J R. 2017. EEG-based brain-computer interfaces. Current Opinion in Biomedical Engineering， 4： 194-200 ［DOI： 10.1016/j.cobme.2017.11.004http://dx.doi.org/10.1016/j.cobme.2017.11.004］

Meng J J， Streitz T， Gulachek N， Suma D and He B. 2018. Three-dimensional brain-computer interface control through simultaneous overt spatial attentional and motor imagery tasks. IEEE Transactions on Biomedical Engineering， 65（11）： 2417-2427 ［DOI： 10.1109/TBME.2018.2872855http://dx.doi.org/10.1109/TBME.2018.2872855］

Morioka H， Kanemura A， Hirayama J I， Shikauchi M， Ogawa T， Ikeda S， Kawanabe M and Ishii S. 2015. Learning a common dictionary for subject-transfer decoding with resting calibration. NeuroImage， 111： 167-178 ［DOI： 10.1016/j.neuroimage.2015.02.015http://dx.doi.org/10.1016/j.neuroimage.2015.02.015］

Müller-Putz G R and Pfurtscheller G. 2008. Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Transactions on Biomedical Engineering， 55（1）： 361-364 ［DOI： 10.1109/TBME.2007.897815http://dx.doi.org/10.1109/TBME.2007.897815］

Mun S， Park M C， Park S and Whang M. 2012. SSVEP and ERP measurement of cognitive fatigue caused by stereoscopic 3D. Neuroscience Letters， 525（2）： 89-94 ［DOI： 10.1016/j.neulet.2012.07.049http://dx.doi.org/10.1016/j.neulet.2012.07.049］

Nakanishi M， Wang Y J， Chen X G， Wang Y T， Gao X R and Jung T P. 2018. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Transactions on Biomedical Engineering， 65（1）： 104-112 ［DOI： 10.1109/TBME.2017.2694818http://dx.doi.org/10.1109/TBME.2017.2694818］

Nakanishi M， Wang Y T， Jung T P， Zao J K， Chien Y Y， Diniz-Filho A， Daga F B， Lin Y P， Wang Y J and Medeiros F A. 2017. Detecting glaucoma with a portable brain-computer interface for objective assessment of visual function loss. JAMA Ophthalmology， 135（6）： 550-557 ［DOI： 10.1001/jamaophthalmol.2017.0738http://dx.doi.org/10.1001/jamaophthalmol.2017.0738］

Ortner R， Allison B Z， Korisek G， Gaggl H and Pfurtscheller G. 2011. An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 19（1）： 1-5 ［DOI： 10.1109/TNSRE.2010.2076364http://dx.doi.org/10.1109/TNSRE.2010.2076364］

Ouyang G， Dien J and Lorenz R. 2022. Handling EEG artifacts and searching individually optimal experimental parameter in real time： a system development and demonstration. Journal of Neural Engineering， 19（1）： #016016 ［DOI： 10.1088/1741-2552/ac42b6http://dx.doi.org/10.1088/1741-2552/ac42b6］

Pan J， Gao X R， Duan F， Yan Z and Gao S K. 2011. Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis. Journal of Neural Engineering， 8（3）： #036027 ［DOI： 10.1088/1741-2560/8/3/036027http://dx.doi.org/10.1088/1741-2560/8/3/036027］

Pan J H， Wang L， Huang H Y， Xiao J， Wang F， Liang Q M， Xu C W， Li Y Q and Xie Q Y. 2022. A hybrid brain-computer interface combining P300 potentials and emotion patterns for detecting awareness in patients with disorders of consciousness. IEEE Transactions on Cognitive and Developmental Systems ［DOI： 10.1109/TCDS.2022.3213194http://dx.doi.org/10.1109/TCDS.2022.3213194］

Pandarinath C， Nuyujukian P， Blabe C H， Sorice B L， Saab J， Willett F R， Hochberg L R， Shenoy K V and Henderson J M. 2017. High performance communication by people with paralysis using an intracortical brain-computer interface. eLife， 6： #18554 ［DOI： 10.7554/eLife.18554http://dx.doi.org/10.7554/eLife.18554］

Phothisonothai M. 2015. An investigation of using SSVEP for EEG-based user authentication system//Proceedings of 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference （APSIPA）. Hong Kong， China： IEEE： 923-926 ［DOI： 10.1109/APSIPA.2015.7415406http://dx.doi.org/10.1109/APSIPA.2015.7415406］

Qiu S， Zhang Y K， Wu C Y， Ma X L， Wei W and He H G. 2021. Research progress of brain computer interface based on fine motor imagery. AI-View，（6）： 40-50

邱爽，张裕坤，吴晨瑶，马学林，魏玮，何晖光. 2021. 基于精细运动想象的脑机接口技术研究进展. 人工智能，（6）： 40-50 ［DOI： 10.16453/j.cnki.ISSN2096-5036.2021.06.005http://dx.doi.org/10.16453/j.cnki.ISSN2096-5036.2021.06.005］

Radecke J O， Schierholz I， Kral A， Lenarz T， Murray M M and Sandmann P. 2022. Distinct multisensory perceptual processes guide enhanced auditory recognition memory in older cochlear implant users. NeuroImage： Clinical， 33： #102942 ［DOI： 10.1016/j.nicl.2022.102942http://dx.doi.org/10.1016/j.nicl.2022.102942］

Ramoser H， Muller-Gerking J and Pfurtscheller G. 2000. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering， 8（4）： 441-446 ［DOI： 10.1109/86.895946http://dx.doi.org/10.1109/86.895946］

Ren S X， Wang W Q， Hou Z G， Liang X， Wang J X and Shi W G. 2020. Enhanced motor imagery based brain-computer interface via FES and VR for lower limbs. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 28（8）： 1846-1855 ［DOI： 10.1109/TNSRE.2020.3001990http://dx.doi.org/10.1109/TNSRE.2020.3001990］

Rocco G， Rix H， Lebrun J， Guetat S， Chanquoy L， Meste O and Magnie-Mauro M N. 2021. Single-trial detection of event-related potentials with integral shape averaging： an application to the elusive N400//Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society （EMBC）. Mexico： IEEE： 1022-1025 ［DOI： 10.1109/EMBC46164.2021.9630271http://dx.doi.org/10.1109/EMBC46164.2021.9630271］

Saffaryazdi N， Wasim S T， Dileep K， Nia A F， Nanayakkara S， Broadbent E and Billinghurst M. 2022. Using facial micro-expressions in combination with EEG and physiological signals for emotion recognition. Frontiers in Psychology， 13： #864047 ［DOI： 10.3389/fpsyg.2022.864047http://dx.doi.org/10.3389/fpsyg.2022.864047］

Sakhavi S， Guan C T and Yan S C. 2018. Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems， 29（11）： 5619-5629 ［DOI： 10.1109/TNNLS.2018.2789927http://dx.doi.org/10.1109/TNNLS.2018.2789927］

Schirrmeister R T， Springenberg J T， Fiederer L D J， Glasstetter M， Eggensperger K， Tangermann M， Hutter F， Burgard W and Ball T. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping， 38（11）： 5391-5420 ［DOI： 10.1002/hbm.23730http://dx.doi.org/10.1002/hbm.23730］

Sellers E W， Krusienski D J， Mcfarland D J， Vaughan T M and Wolpaw J R. 2006. A P300 event-related potential brain-computer interface （BCI）： the effects of matrix size and inter stimulus interval on performance. Biological Psychology， 73（3）： 242-252 ［DOI： 10.1016/j.biopsycho.2006.04.007http://dx.doi.org/10.1016/j.biopsycho.2006.04.007］

Shen X K， Liu X G， Hu X， Zhang D and Song S. 2022. Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition. IEEE Transactions on Affective Computing ［DOI： 10.48550/arXiv.2109.09559http://dx.doi.org/10.48550/arXiv.2109.09559］

Shetty A， Hebbar S P， Shenoy R， Peter V and Krishnan G. 2022. A prime-masked ERP investigation on phonology in visual word processing among bilingual speakers of alphasyllabic and alphabetic orthographies. Scientific Reports， 12（1）： #9870 ［DOI： 10.1038/s41598-022-13654-8http://dx.doi.org/10.1038/s41598-022-13654-8］

Shi N L， Wang L P， Chen Y H， Yan X Y， Yang C， Wang Y J and Gao X R. 2020. Steady-state visual evoked potential （SSVEP）-based brain-computer interface （BCI） of Chinese speller for a patient with amyotrophic lateral sclerosis： a case report. Journal of Neurorestoratology， 8（1）： 40-52 ［DOI： 10.26599/JNR.2020.9040003http://dx.doi.org/10.26599/JNR.2020.9040003］

Shih J J， Krusienski D J and Wolpaw J R. 2012. Brain-computer interfaces in medicine. Mayo Clinic Proceedings， 87（3）： 268-279 ［DOI： 10.1016/j.mayocp.2011.12.008http://dx.doi.org/10.1016/j.mayocp.2011.12.008］

Sicard V， Harrison A T and Moore R D. 2021. Psycho-affective health， cognition， and neurophysiological functioning following sports-related concussion in symptomatic and asymptomatic athletes， and control athletes. Scientific Reports， 11（1）： #13838 ［DOI： 10.1038/s41598-021-93218-4http://dx.doi.org/10.1038/s41598-021-93218-4］

Sosulski J， Kemmer J P and Tangermann M. 2021. Improving covariance matrices derived from tiny training datasets for the classification of event-related potentials with linear discriminant analysis. Neuroinformatics， 19（3）： 461-476 ［DOI： 10.1007/s12021-020-09501-8http://dx.doi.org/10.1007/s12021-020-09501-8］

Steidtmann D， Ingram R E and Siegle G J. 2010. Pupil response to negative emotional information in individuals at risk for depression. Cognition and Emotion， 24（3）： 480-496 ［DOI： 10.1080/02699930902738897http://dx.doi.org/10.1080/02699930902738897］

Tan Y， Zang B Y， Lin Y F and Gao X R. 2021. A convolution network of multi-windows spatial-temporal feature analysis for single-trial EEG classification in RSVP task//Proceedings of the 14th International Congress on Image and Signal Processing， BioMedical Engineering and Informatics （CISP-BMEI）. Shanghai， China： IEEE： 1-6 ［DOI： 10.1109/CISP-BMEI53629.2021.9624450http://dx.doi.org/10.1109/CISP-BMEI53629.2021.9624450］

Tang Y C， Zhang J J， Corballis P M and Hallum L E. 2021. Towards the classification of error-related potentials using riemannian geometry//Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society （EMBC）. Mexico： IEEE： 5905-5908 ［DOI： 10.1109/EMBC46164.2021.9629583http://dx.doi.org/10.1109/EMBC46164.2021.9629583］

Tang Z C， Zhang L T， Chen X， Ying J C， Wang X Y and Wang H. 2022. Wearable supernumerary robotic limb system using a hybrid control approach based on motor imagery and object detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 30： 1298-1309 ［DOI： 10.1109/TNSRE.2022.3172974http://dx.doi.org/10.1109/TNSRE.2022.3172974］

Tangermann M， Müller K R， Aertsen A， Birbaumer N， Braun C， Brunner C， Leeb R， Mehring C， Miller K J， Müller-Putz G R， Nolte G， Pfurtscheller G， Preissl H， Schalk G， Schlögl A， Vidaurre C， Waldert S and Blankertz B. 2012 review of the BCI competition IV. Frontiers in Neuroscience， 6： #55 ［DOI： 10.3389/fnins.2012.00055http://dx.doi.org/10.3389/fnins.2012.00055］

Townsend G and Platsko V. 2016. Pushing the P300-based brain-computer interface beyond 100 bpm： extending performance guided constraints into the temporal domain. Journal of Neural Engineering， 13（2）： #026024 ［DOI： 10.1088/1741-2560/13/2/026024http://dx.doi.org/10.1088/1741-2560/13/2/026024］

Valstar M， Schuller B， Smith K， Eyben F， Jiang B， Bilakhia S， Schnieder S， Cowie R and Pantic M. 2013. Avec 2013： the continuous audio/visual emotion and depression recognition challenge//Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge. Barcelona， Spain： ACM： #2512533 ［DOI： 10.1145/2512530.2512533http://dx.doi.org/10.1145/2512530.2512533］

Vařeka L and Ladouce S. 2021. Prediction of navigational decisions in the real-world： a visual P300 event-related potentials brain-computer interface. International Journal of Human-Computer Interaction， 37（14）： 1375-1389 ［DOI： 10.1080/10447318.2021.1888510http://dx.doi.org/10.1080/10447318.2021.1888510］

Velasco-Álvarez F， Fern􀆦ndez-Rodríguez Á， Medina-Juli􀆦 M T and Ron-Angevin R. 2021a. Speech stream segregation to control an ERP-based auditory BCI. Journal of Neural Engineering， 18（2）： #026023 ［DOI： 10.1088/1741-2552/abdd44http://dx.doi.org/10.1088/1741-2552/abdd44］

Velasco-Álvarez F， Fern􀆦ndez-Rodríguez Á and Ron-Angevin R. 2022. Brain-computer interface （BCI）-generated speech to control domotic devices. Neurocomputing， 509： 121-136 ［DOI： 10.1016/j.neucom.2022.08.068http://dx.doi.org/10.1016/j.neucom.2022.08.068］

Velasco-Álvarez F， Fern􀆦ndez-Rodríguez Á， Vizcaíno-Martín F J， Díaz-Estrella A and Ron-Angevin R. 2021b. Brain-computer interface （BCI） control of a virtual assistant in a smartphone to manage messaging applications. Sensors， 21（11）： #3716 ［DOI： 10.3390/s21113716http://dx.doi.org/10.3390/s21113716］

Vidal J J. 1973. Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering， 2（1）： 157-180 ［DOI： 10.1146/annurev.bb.02.060173.001105http://dx.doi.org/10.1146/annurev.bb.02.060173.001105］

Volosyak I， Gembler F and Stawicki P. 2017. Age-related differences in SSVEP-based BCI performance. Neurocomputing， 250： 57-64 ［DOI： 10.1016/j.neucom.2016.08.121http://dx.doi.org/10.1016/j.neucom.2016.08.121］

Wang F， Zhang W W， Xu Z F， Ping J Y and Chu H. 2021. A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition. Neural Computing and Applications， 33（15）： 9061-9073 ［DOI： 10.1007/s00521-020-05670-4http://dx.doi.org/10.1007/s00521-020-05670-4］

Wang P P， Wang M L， Zhou Y Y， Xu Z M and Zhang D Q. 2022. Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network. Frontiers of Computer Science， 16（5）： #165328 ［DOI： 10.1007/s11704-021-0587-2http://dx.doi.org/10.1007/s11704-021-0587-2］

Wang P T， Lu J， Zhang B and Tang Z. 2015. A review on transfer learning for brain-computer interface classification//Proceedings of the 5th International Conference on Information Science and Technology （ICIST）. Changsha， China： IEEE： 315-322 ［DOI： 10.1109/ICIST.2015.7288989http://dx.doi.org/10.1109/ICIST.2015.7288989］

Wei W， Qiu S， Ma X L， Li D， Wang B and He H G. 2020. Reducing calibration efforts in rsvp tasks with multi-source adversarial domain adaptation. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 28（11）： 2344-2355 ［DOI： 10.1109/TNSRE.2020.3023761http://dx.doi.org/10.1109/TNSRE.2020.3023761］

Wei W， Qiu S， Zhang Y K， Mao J Y and He H G. 2022. ERP prototypical matching net： a meta-learning method for zero-calibration RSVP-based image retrieval. Journal of Neural Engineering， 19（2）： #026028 ［DOI： 10.1088/1741-2552/ac5eb7http://dx.doi.org/10.1088/1741-2552/ac5eb7］

Willett F R， Avansino D T， Hochberg L R， Henderson J M and Shenoy K V. 2021. High-performance brain-to-text communication via handwriting. Nature， 593（7858）： 249-254 ［DOI： 10.1038/s41586-021-03506-2http://dx.doi.org/10.1038/s41586-021-03506-2］

Wolpaw J R， Birbaumer N， Mcfarland D J， Pfurtscheller G and Vaughan T M. 2002. Brain-computer interfaces for communication and control. Clinical Neurophysiology， 113（6）： 767-791 ［DOI： 10.1016/S1388-2457（02）00057-3http://dx.doi.org/10.1016/S1388-2457（02）00057-3］

Wolpaw J R and Wolpaw E W. 2017. Brain-Computer Interfaces： Principles and Practice. Fu Y F， Yang Q H， Xu B L and Li Y C， trans. Beijing： National Defense Industry Press

Wolpaw J R and Wolpaw E W. 2017. 脑—机接口：原理与实践. 伏云发，杨秋红，徐宝磊，李永程，译. 北京：国防工业出版社

Wong C M， Wan F， Wang B Y， Wang Z， Nan W Y， Lao K F， Mak P U， Vai M I and Rosa A. 2020. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. Journal of Neural Engineering， 17（1）： #016026 ［DOI： 10.1088/1741-2552/ab2373http://dx.doi.org/10.1088/1741-2552/ab2373］

Wong C M， Wang Z， Nakanishi M， Wang B Y， Rosa A， Chen C L P， Jung T P and Wan F. 2022. Online adaptation boosts SSVEP-based BCI performance. IEEE Transactions on Biomedical Engineering， 69（6）： 2018-2028 ［DOI： 10.1109/TBME.2021.3133594http://dx.doi.org/10.1109/TBME.2021.3133594］

Wu D R， Xu Y F and Lu B L. 2022. Transfer learning for EEG-based brain–computer interfaces： a review of progress made since 2016. IEEE Transactions on Cognitive and Developmental Systems， 14（1）： 4-19 ［DOI： 10.1109/TCDS.2020.3007453http://dx.doi.org/10.1109/TCDS.2020.3007453］

Xia K， Deng L F， Duch W and Wu D R. 2022. Privacy-preserving domain adaptation for motor imagery-based brain-computer interfaces. IEEE Transactions on Biomedical Engineering， 69（11）： 3365-3376 ［DOI： 10.1109/TBME.2022.3168570http://dx.doi.org/10.1109/TBME.2022.3168570］

Xiao J， He Y B， Yu T Y， Pan J H， Xie Q Y， Cao C Y， Zheng H Y， Huang W T， Gu Z H， Yu Z L and Li Y Q. 2022. Toward assessment of sound localization in disorders of consciousness using a hybrid audiovisual brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 30： 1422-1432 ［DOI： 10.1109/TNSRE.2022.3176354http://dx.doi.org/10.1109/TNSRE.2022.3176354］

Xiao X L， Xu M P， Han J， Yin E W， Liu S， Zhang X， Jung T P and Ming D. 2021. Enhancement for P300-speller classification using multi-window discriminative canonical pattern matching. Journal of Neural Engineering， 18（4）： #046079 ［DOI： 10.1088/1741-2552/ac028bhttp://dx.doi.org/10.1088/1741-2552/ac028b］

Xu M P， Han J， Wang Y J， Jung T P and Ming D. 2020. Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features. IEEE Transactions on Biomedical Engineering， 67（11）： 3073-3082 ［DOI： 10.1109/TBME.2020.2975614http://dx.doi.org/10.1109/TBME.2020.2975614］

Yadav S， Saha S K， Kar R and Mandal D. 2022. EEG/ERP signal enhancement through an optimally tuned adaptive filter based on marine predators algorithm. Biomedical Signal Processing and Control， 73： #103427 ［DOI： 10.1016/j.bspc.2021.103427http://dx.doi.org/10.1016/j.bspc.2021.103427］

Yan W Q， Wu Y C， Du C H and Xu G H. 2022. An improved cross-subject spatial filter transfer method for SSVEP-based BCI. Journal of Neural Engineering， 19（4）： #046028 ［DOI： 10.1088/1741-2552/ac81eehttp://dx.doi.org/10.1088/1741-2552/ac81ee］

Yan X， Zhao L M and Lu B L. 2021. Simplifying multimodal emotion recognition with single eye movement modality//Proceedings of the 29th ACM International Conference on Multimedia. Chengdu， China： ACM： 1057-1063 ［DOI： 10.1145/3474085.3475701http://dx.doi.org/10.1145/3474085.3475701］

Yang B H， Ma J， Qiu W Z， Zhang J and Wang X F. 2022. The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network. Biomedical Signal Processing and Control， 78： #103855 ［DOI： 10.1016/j.bspc.2022.103855http://dx.doi.org/10.1016/j.bspc.2022.103855］

Yang C， Han X， Wang Y J， Saab R， Gao S K and Gao X R. 2018. A dynamic window recognition algorithm for SSVEP-based brain-computer interfaces using a spatio-temporal equalizer. International Journal of Neural Systems， 28（10）： #1850028 ［DOI： 10.1142/S0129065718500284http://dx.doi.org/10.1142/S0129065718500284］

Yang C， Yan X Y， Wang Y J， Chen Y H， Zhang H X and Gao X R. 2021. Spatio-temporal equalization multi-window algorithm for asynchronous SSVEP-based BCI. Journal of Neural Engineering， 18（4）： #0460b7 ［DOI： 10.1088/1741-2552/ac127fhttp://dx.doi.org/10.1088/1741-2552/ac127f］

Yang C， Zhang H X， Zhang S G， Han X， Gao S K and Gao X R. 2020. The spatio-temporal equalization for evoked or event-related potential detection in multichannel EEG data. IEEE Transactions on Biomedical Engineering， 67（8）： 2397-2414 ［DOI： 10.1109/TBME.2019.2961743http://dx.doi.org/10.1109/TBME.2019.2961743］

Yin A， Tseng P H， Rajangam S， Lebedev M A and Nicolelis M A L. 2018. Place cell-like activity in the primary sensorimotor and premotor cortex during monkey whole-body navigation. Scientific Reports， 8（1）： #9184 ［DOI： 10.1038/s41598-018-27472-4http://dx.doi.org/10.1038/s41598-018-27472-4］

Yu T Y， Xiao J， Wang F Y， Zhang R， Gu Z H， Cichocki A and Li Y Q. 2015. Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Transactions on Biomedical Engineering， 62（7）： 1706-1717 ［DOI： 10.1109/TBME.2015.2402283http://dx.doi.org/10.1109/TBME.2015.2402283］

Zhang D and Li J W. 2017. Exploring the power of mind： status and prospect of brain-computer interfaces. Science and Technology Review， 35（9）： 62-67

张丹，李佳蔚. 2021. 探索思维的力量：脑机接口研究现状与展望. 科技导报， 35（9）： 62-67 ［DOI： 10.3981/j.issn.1000-7857.2017.09.008http://dx.doi.org/10.3981/j.issn.1000-7857.2017.09.008］

Zhang K S， Robinson N， Lee S W and Guan C T. 2021a. Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Networks， 136： 1-10 ［DOI： 10.1016/j.neunet.2020.12.013http://dx.doi.org/10.1016/j.neunet.2020.12.013］

Zhang S， Tang C G and Guan C T. 2022b. Visual-to-EEG cross-modal knowledge distillation for continuous emotion recognition. Pattern Recognition， 130： #108833 ［DOI： 10.1016/j.patcog.2022.108833http://dx.doi.org/10.1016/j.patcog.2022.108833］

Zhang S G， Chen X G， Wang Y J， Liu B L and Gao X R. 2022a. Visual field inhomogeneous in brain-computer interfaces based on rapid serial visual presentation. Journal of Neural Engineering， 19（1）： #016015 ［DOI： 10.1088/1741-2552/ac4a3ehttp://dx.doi.org/10.1088/1741-2552/ac4a3e］

Zhang X Y， Qiu S， Zhang Y K， Wang K N， Wang Y J and He H G. 2022c. Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs. Journal of Neural Engineering， 19（4）： #046027 ［DOI： 10.1088/1741-2552/ac823ehttp://dx.doi.org/10.1088/1741-2552/ac823e］

Zhang Y， Cheng C and Zhang Y D. 2021b. Multimodal emotion recognition using a hierarchical fusion convolutional neural network. IEEE Access， 9： 7943-7951 ［DOI： 10.1109/ACCESS.2021.3049516http://dx.doi.org/10.1109/ACCESS.2021.3049516］

Zhang Y， Zhou G X， Jin J， Wang M J， Wang X Y and Cichocki A. 2013. L1-regularized multiway canonical correlation analysis for SSVEP-based BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering， 21（6）： 887-896 ［DOI： 10.1109/tnsre.2013.2279680http://dx.doi.org/10.1109/tnsre.2013.2279680］

Zhang Y， Zhou G X， Jin J， Wang X Y and Cichocki A. 2014. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. International Journal of Neural Systems， 24（4）： #1450013 ［DOI： 10.1142/S0129065714500130http://dx.doi.org/10.1142/S0129065714500130］

Zhang Y B， Shen H， Li M and Hu D W. 2022d. Brain biometrics of steady state visual evoked potential functional networks. IEEE Transactions on Cognitive and Developmental Systems ［DOI： 10.1109/TCDS.2022.3160295http://dx.doi.org/10.1109/TCDS.2022.3160295］

Zhang Y S， Guo D Q， Yao D Z and Xu P. 2017. The extension of multivariate synchronization index method for SSVEP-based BCI. Neurocomputing， 269： 226-231 ［DOI： 10.1016/j.neucom.2017.03.082http://dx.doi.org/10.1016/j.neucom.2017.03.082］

Zhao L M， Yan X and Lu B L. 2021a. Plug-and-play domain adaptation for cross-subject EEG-based emotion recognition. Proceedings of the AAAI Conference on Artificial Intelligence， 35（1）： 863-870 ［DOI： 10.1609/aaai.v35i1.16169http://dx.doi.org/10.1609/aaai.v35i1.16169］

Zhao X， Wang Z Y， Zhang M and Hu H L. 2021b. A comfortable steady state visual evoked potential stimulation paradigm using peripheral vision. Journal of Neural Engineering， 18（5）： #056021 ［DOI： 10.1088/1741-2552/abf397http://dx.doi.org/10.1088/1741-2552/abf397］

Zheng W L， Liu W， Lu Y F， Lu B L and Cichocki A. 2019. EmotionMeter： a multimodal framework for recognizing human emotions. IEEE Transactions on Cybernetics， 49（3）： 1110-1122 ［DOI： 10.1109/TCYB.2018.2797176http://dx.doi.org/10.1109/TCYB.2018.2797176］

Zheng W L and Lu B L. 2016. Personalizing EEG-based affective models with transfer learning//Proceedings of the 25th International Joint Conference on Artificial Intelligence. New York， USA： AAAI Press： 2732-2738

Zhou X Y， Xu M P， Xiao X L， Wang Y J， Jung T P and Ming D. 2021. Detection of fixation points using a small visual landmark for brain-computer interfaces. Journal of Neural Engineering， 18（4）： #046098 ［DOI： 10.1088/1741-2552/ac0b51http://dx.doi.org/10.1088/1741-2552/ac0b51］

Zhou Y J， He S H， Huang Q Y and Li Y Q. 2020. A hybrid asynchronous brain-computer interface combining SSVEP and EOG signals. IEEE Transactions on Biomedical Engineering， 67（10）： 2881-2892 ［DOI： 10.1109/TBME.2020.2972747http://dx.doi.org/10.1109/TBME.2020.2972747］

Zhu Y L， Li Y， Lu J L and Li P C. 2021. EEGNet with ensemble learning to improve the cross-session classification of SSVEP based BCI from Ear-EEG. IEEE Access， 9： 15295-15303 ［DOI： 10.1109/ACCESS.2021.3052656http://dx.doi.org/10.1109/ACCESS.2021.3052656］

文章被引用时，请邮件提醒。

提交

暂无数据