[1] 唐孝威. 脑科学导论[M]. 杭州:浙江大学出版社, 2006. Tang Xiaowei. Introduction to brain science[M]. Hangzhou:Zhe-jiang University Press, 2006.
[2] Poo M M, Du J L, Ip N Y, et al. China Brain Project:Basic neuroscience, brain diseases, and brain-inspired computing[J]. Neuron, 2016, 92(3):591-596.
[3] Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-com-puter interfaces for communication and control[J]. Clinical Neu-rophysiology, 2002, 113(6):767-791.
[4] Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-com-puter interface technology:A review of the first international meeting[J]. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2):164-173.
[5] Wolpaw J, Wolpaw E W. Brain-computer interfaces:Principles and practice[M]. Oxford:Oxford University Press, 2012.
[6] Müller-Putz G R, Daly I, Kaiser V. Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain-computer interface accuracy[J]. Journal of Neural Engineering, 2014, 11(3):035011.
[7] Mainsah B O, Collins L M, Colwell K A, et al. Increasing BCI communication rates with dynamic stopping towards more prac-tical use:An ALS study[J]. Journal of Neural Engineering, 2015, 12(1):016013.
[8] Höhne J, Holz E, Staiger-Sälzer P, et al. Motor imagery for se-verely motor-impaired patients:Evidence for brain-computer interfacing as superior control solution[J]. PLoS One, 2014, 9:e104854-e104854.
[9] Burns A, Adeli H, Buford J A. Brain-computer interface after nervous system injury[J]. Neuroscientist, 2014, 20(6):639-651.
[10] Thompson D E, Quitadamo L R, Mainardi L, et al. Perfor-mance measurement for brain-computer or brain-machine in-terfaces:A tutorial[J]. Journal of Neural Engineering, 2014, 11(3):035001.
[11] Cecotti H. Spelling with non-invasive Brain-computer inter-faces-current and future trends[J]. Journal of Physiology-Par-is, 2011, 105(1):106-114.
[12] Kaiser V, Kreilinger A, Müller-Putz G R, et al. First steps to-ward a motor imagery based stroke BCI:New strategy to set up a classifier[J]. Frontiers in Neuroscience, 2011, 5:86.
[13] Tangwiriyasakul C, Mocioiu V, Van Putten M J, et al. Classifi-cation of motor imagery performance in acute stroke[J]. Jour-nal of Neural Engineering, 2014, 11(3):036001.
[14] Nicolas-Alanso L F, Corralejo R, Gomez-Pilar J, et al. Adap-tive stacked generalization for multiclass motor imagerybased brain computer interfaces[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(4):702-712.
[15] 明东, 王坤, 何峰, 等. 想象动作诱发生理信息检测及其应用研究:回顾与展望[J]. 仪器仪表学报, 2014, 35(9):1921-1931. Ming Dong, Wang Kun, He Feng, et al. Study on physiologi-cal information detection and application evoked by motor im-agery:Review and prospect[J]. Chinese Journal of Scientific Instrument, 2014, 35(9):1921-1931.
[16] Mccane L M, Heckman S M, Mcfarland D J, et al. P300-based brain-computer interface (BCI) event-related potentials (ERPs):People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls[J]. Clinical Neurophysiology, 2015, 126(11):2124-2131.
[17] Xu M, Chen L, Zhang L, et al. A visual parallel-BCI speller based on the time-frequency coding strategy[J]. Journal of Neural Engineering, 2014, 11(2):026014.
[18] Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface[J]. Proceedings of the National Academy of Sciences, 2015, 112(44):E6058-E6067.
[19] Yi W, Shuang Q, Qi H, et al. EEG feature comparison and classification of simple and compound limb motor imagery[J]. Journal of Neuroengineering & Rehabilitation, 2013, 10(8):541-544.
[20] Simon N, Käthner I, Ruf C A, et al. An auditory multiclass brain-computer interface with natural stimuli:Usability evalu-ation with healthy participants and a motor impaired end user[J]. Frontiers in Human Neuroscience, 2014, 8(1039):1039.
[21] Kaufmann T, Herweg A, Kübler A. Toward brain-computer in-terface based wheelchair control utilizing tactually-evoked event-related potentials[J]. Journal of Neuroengineering & Re-habilitation, 2014, 11(1):7.
[22] Elnady A M, Zhang X, Xiao Z G, et al. A single-session pre-liminary evaluation of an affordable BCI-controlled arm exo-skeleton and motor-proprioception platform[J]. Frontiers in Human Neuroscience, 2015, 9:168.
[23] Sczesny-Kaiser M, Höffken O, Aach M, et al. HAL® exoskel-eton training improves walking parameters and normalizes cor-tical excitability in primary somatosensory cortex in spinal cord injury patients[J]. Journal of Neuroengineering and Reha-bilitation, 2015, 12(1):68-78.
[24] Van Asseldonk E H, Boonstra T A. Transcranial direct cur-rent stimulation of the leg motor cortex enhances coordinated motor output during walking with a large inter-individual vari-ability[J]. Brain Stimulation, 2015, 9(2):182-190.
[25] Torres J, Drebing D, Hamilton R. TMS and tDCS in poststroke aphasia:Integrating novel treatment approaches with mechanisms of plasticity[J]. Restorative Neurology & Neuro-science, 2013, 31(4):501-515.
[26] Chen L, Wang Z, He F, et al. An online hybrid brain-comput-er interface combining multiple physiological signals for web-page browse[C]//Engineering in Medicine and Biology Society (EMBC), 201537th Annual International Conference of the IEEE. Piscataway NJ:IEEE, 2015:1152-1155.
[27] Throckmorton C S, Colwell K A, Ryan D B, et al. Bayesian approach to dynamically controlling data collection in P300 spellers[J]. IEEE Transactions on Neural Systems and Reha-bilitation Engineering, 2013, 21(3):508-517.
[28] Smith S. Mind-controlled exoskeleton kicks off world cup[J]. New Scientist, 2014, 6(13):2973.
[29] Xu M, Qi H, Wan B, et al. A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature[J]. Journal of Neural Engineering, 2013, 10(2):026001.
[30] Rupp R. Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury[J]. Frontiers in Neuroengineering, 2014, 7(7):38.
[31] Kindermans P J, Tangermann M, Müller K R, et al. Integrat-ing dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller[J]. Journal of Neural Engineering, 2014, 11(3):035005.
[32] Cantillo-Negrete J, Gutierrez-Martinez J, Carino-Escobar R I, et al. An approach to improve the performance of subjectindependent BCIs-based on motor imagery allocating subjects by gender[J]. Biomedical Engineering Online, 2014, 13(1):158.
[33] Woehrle H, Krell M M, Straube S, et al. An adaptive spatial filter for user-independent single trial detection of event-re-lated potentials[J]. IEEE Transactions on Biomedical Engi-neering, 2015, 62:1696-1705.
[34] Bryan M J, Martin S A, Cheung W, et al. Probabilistic coadaptive brain-computer interfacing[J]. Journal of Neural En-gineering, 2013, 10(6):066008.
[35] Kee C Y, Chetty R M K, Khoo B H, et al. Genetic algorithm and bayesian linear discriminant analysis based channel selec-tion method for P300 BCI[J]. Communications in Computer & Information Science, 2012, 330:226-235.
[36] Huggins J E, Wolpaw J R. Papers from the fifth international brain-computer interface meeting[J]. Journal of Neural Engi-neering, 2014, 11(3):030301-1-2.
[37] Pfurtscheller G, Neuper C, Müller G R, et al. Graz-BCI:State of the art and clinical applications[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2003, 11(2):177-180.
[38] Im H, Ku J, Kim H J, et al. Virtual reality-guided motor im-agery increases corticomotor excitability in healthy volunteers and stroke patients[J]. Annals of Rehabilitation Medicine, 2016, 40(3):420-431.
[39] Kaplan A, Shishkin S, Ganin I, et al. Adapting the P300-based brain-computer interface for gaming:A review[J]. IEEE Transactions on Computational Intelligence and AI in Games, 2013, 5(2):141-149.
[40] Miralles F, Vargiu E, Dauwalder S, et al. Brain computer in-terface on track to home[J]. Scientific World Journal, 2015, doi:10.1155/2015/623896.
[41] Holz E M, Botrel L, Kaufmann T, et al. Long-term indepen-dent brain-computer interface home use improves quality of life of a patient in the locked-in state:A case study[J]. Ar-chives of Physical Medicine and Rehabilitation, 2015, 96(3):S16-S26.