1 |
Curado M R, Cossio E G, Broetz D, et al. Residual upper arm motor function primes innervation of paretic forearm muscles in chronic stroke after brain-machine interface (BMI) training [J]. PLoS One, 2015, 10(10): e0140161.
|
2 |
Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation [J]. Lancet, 2011, 377(9778): 1693-1702.
|
3 |
López-Larraz E, Sarasola-Sanz A, Irastorza-Landa N, et al. Brain-machine interfaces for rehabilitation in stroke: a review [J]. NeuroRehabilitation, 2018, 43(1): 77-97.
|
4 |
Coscia M, Maximilian J W, Chaudary U, et al. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke [J]. Brain, 2019, 142(8): 2182-2197.
|
5 |
Veerbeek J M, Langbroek-Amersfoort A C, van Wegen E E H, et al. Effects of robot-assisted therapy for the upper limb after stroke [J]. Neurorehabil Neural Repair, 2017, 31(2): 107-121.
|
6 |
Pollock A, Farmer S E, Brady M C, et al. Interventions for improving upper limb function after stroke [J]. Cochrane Database Syst Rev, 2014(11): CD010820.
|
7 |
Raffin E, Hummel F C. Restoring motor functions after stroke: multiple approaches and opportunities [J]. Neuroscientist, 2018, 24(4): 400-416.
|
8 |
Wu X, Guarino P, Lo A C, et al. Long-term effect-iveness of intensive therapy in chronic stroke [J]. Neurorehabil Neural Repair, 2016, 30(6): 583-590.
|
9 |
Bell J A, Wolke M L, Ortez R C, et al. Training intensity affects motor rehabilitation efficacy following unilateral ischemic insult of the sensorimotor cortex in C57BL/6 mice [J]. Neurorehabil Neural Repair, 2015, 29(6): 590-598.
|
10 |
Byblow W D, Stinear C M, Barber P A, et al. Proportional recovery after stroke depends on corticomotor integrity [J]. Ann Neurol, 2015, 78(6): 848-859.
|
11 |
Buch E R, Rizk S, Nicolo O, et al. Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging [J]. Neurology, 2016, 86(20): 1924-1925.
|
12 |
Guggisberg A G, Nicolo P, Cohen L G, et al. Longitudinal structural and functional differences between proportional and poor recovery after stroke [J]. Neurorehabil Neural Repair, 2017, 31(12): 1029-1041.
|
13 |
Winters C, van Wegen E E, Daffertshofer A, et al. Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke [J]. Neurorehabil Neural Repair, 2015, 29(7): 614-622.
|
14 |
Chen L C, Sandmann P, Thorne J D, et al. Association of concurrent sNIRS and EEG signatures in response to auditory and visual stimuli [J]. Brain Topogr, 2015, 28(5): 710-725.
|
15 |
Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control [J]. Clin Neurophysiol, 2002, 113(6): 767-791.
|
16 |
Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain-computer interfaces for communication and rehabilitation [J]. Nat Rev Neurol, 2016, 12(9): 513-525.
|
17 |
Lebedev M A, Nicolelis M A. Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation [J]. Physiol Rev, 2017, 97(2): 767-837.
|
18 |
Bouton C E, Shaikhouni A, Anneta N V, et al. Restoring cortical control of functional movement in a human with quadriplegia [J]. Nature, 2016, 533(7602): 247-250.
|
19 |
Mestais C S, Charvet G, Sauter-Starace F, et al. WIMAGINE: Wireless 64-channel ECoG recording inplant for long term clinical applications [J]. IEEE Trans Neural Syst Rehabil Eng, 2015, 23(1): 10-21.
|
20 |
Suner S, Fellows M R, Vargas-Irwin C, et al. Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex [J]. IEEE Trans Neural Syst Rehabil Eng, 2005, 13(4): 524-541.
|
21 |
Borton D A, Yin M, Aceros J, et al. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates [J]. J Neural Eng, 2013, 10(2): 026010.
|
22 |
琚芬,赵晨光,袁华. 脑机接口在康复医学中的应用进展[J]. 中国康复, 2017, 32(6): 508-511.
|
|
Ju F, Zhao C G, Yuan H, et al. Application progress of brain-computer interface in rehabilitation medicine [J]. Chin J Rehabil, 2017, 32(6): 508-511.
|
23 |
Li X, Samuel O W, Zhang X, et al. A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees [J]. J Neuroeng Rehabil, 2017, 14: 2.
|
24 |
Berger A, Horst F, Müller S, et al. Current state and future prospects of EEG and fNIRS in robot-assisted gait rehabilitation: a brief review [J]. Front Hum Neurosci, 2019, 13: 172.
|
25 |
Sburlea A I, Montesano L, de la Cuerda R C, et al. Detecting intention to walk in stroke patients from pre-movement EEG correlates [J]. J Neuroeng Rehabil, 2015, 12: 113.
|
26 |
Ofner P, Schwarz A, Pereira J. Upper limb movements can be decoded from the time-domain of low-frequency EEG [J]. PLoS One, 2017, 12(8): e0182578.
|
27 |
Shiman F, López-Larraz E, Sarasola-Sanz A, et al. Classification of different reaching movements from the same limb using EEG [J]. J Neural Eng, 2017, 14(4): 046018.
|
28 |
López-Larraz E, Ibáñez J, Trincado-Alonso F, et al. Comparing recalibration strategies for electroencephalography-based decoders of movement intention in neurological patients with motor disability [J]. Int J Neural Syst, 2018, 28(7): 1750060.
|
29 |
Insausti-Delgado A, López-Larraz E, Bibián C, et al. Influence of trans-spinal magnetic stimulation in electrophysiological recording for closed-loop rehabilitative systems [J]. Annu Int Conf IEEE Eng Med Biol Soc, 2017, 2017: 2518-2521.
|
30 |
López-Larraz E, Bibián C, Birbaumer N, et al. Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients [J]. IEEE Int Conf Rehabil Robot, 2017, 2017: 901-906.
|
31 |
Ang K K, Chua K S, Phua K S, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke [J]. Clin EEG Neurosci, 2015, 46(4): 310-320.
|
32 |
Krebs H I, Palazzolo J J, Dipietro L, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy [J]. Autonomous Robots, 2003, 15(1): 7-20.
|
33 |
Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study [J]. Ann Neurol, 2013, 74(1): 100-108.
|
34 |
McConnell A C, Moioli R C, Brasil F L, et al. Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke [J]. J Rehabil Med, 2017, 49(6): 449-460.
|
35 |
Ganguly K, Secundo L, Ranade G, et al. Cortical representation of ipsilateral arm movements in monkey and man [J]. J Neurosci, 2009, 29(41): 12948-12956.
|
36 |
Antelis J M, Montesano L, Ramos-Murguiaday A, et al. Decoding upper limb movement attempt from EEG measurements of the contralesional motor cortex in chronic stroke patients [J]. IEEE Trans Biomed Eng, 2017, 64(1): 99-111.
|
37 |
Marin-Pardo O, Laine C M, Rennie M, et al. A virtually reality muscle-computer interface for neurorehabilitation in chronic stroke: a pilot study [J]. Sensors, 2020, 20: 3754.
|
38 |
Ramos-Murguialday A, Curado M R, Broetz D, et al. Brain-machine-interface in chronic stroke: randomised trial long-term follow-up [J]. Neurorehabil Neural Repair, 2019, 33(3): 188-198.
|
39 |
Frolov A A, Mokienko O, Lyukmanov R, et al. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI) -controlled exoskeleton: a randomized controlled multicenter trial [J]. Front Neurosci, 2017, 11: 400.
|
40 |
Kim T, Kim S, Lee B. Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial [J]. Occup Ther Int, 2016, 23(1): 39-47.
|
41 |
Mrachacz-Kersting N, Jiang N, Stevenson A J, et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface [J]. J Neurophysiol, 2016, 115(3): 1410-1421.
|
42 |
Biasiucci A, Leeb R, Iturrate I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke [J]. Nat Commun, 2018, 9(1): 2421.
|
43 |
Carvalho R, Dias N, Cerqueira J J. Brain-machine interface of upper limb recovery in stroke patients rehabilitation: a systematic review [J]. Physiother Res Int, 2019, 24: e1764.
|
44 |
Ang K K, Guan C, Phua K S, et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: result of a three-armed randomized controlled trial for chronic stroke [J]. Front Neuroeng, 2014, 7: 30.
|
45 |
Cervera M A, Soekadar S R, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis [J]. Ann Clin Transl Neurol, 2018, 5(5): 651-663.
|
46 |
Mugler E M, Tomic G, Singh A, et al. Myoelectric computer interface training for reducing co-activative and enhancing arm movement in chronic stroke survivors: a randomized trial [J]. Neurorehabil Neural Repair, 2019, 33(4): 284-295.
|
47 |
Vourvopoulos A, Pardo O M, Lefebvre S, et al. Effects of a brain-computer interface with virtual reality (VR) neurofeedback: a pilot study in chronic stroke patients [J]. Front Hum Neurosci, 2019, 13: 1-17.
|
48 |
Morone G, Pisotta I, Pichiorri F, et al. Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability [J]. Arch Phys Med Rehabil, 2015, 96(3): S71-S78.
|
49 |
Soekadar S R, Birbaumer N, Slutzky M W, et al. Brain-machine interface in neurorehabilitation of stroke [J]. Neurobiol Dis, 2015, 83: 172-179.
|
50 |
Li M, Liu Y, Wu Y, et al. Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training [J]. Int J Neurosci, 2014, 124(6): 403-415.
|
51 |
Waldert S. Invasive vs. non-invasive neuronal signals for brain-machine interfaces: will one prevail? [J]. Front Neurosci, 2016, 10: 295.
|
52 |
宁晓路,曹永福,张颖,等. 脑机接口技术应用的伦理问题分析[J]. 医学与哲学, 2018, 39(9A): 35-38.
|
|
Ning X L, Cao Y F, Zhang Y, et al. Analysis of ethical issues in the application of brain-computer interface technology [J]. Med Philosophy, 2018, 39(9A): 35-38.
|
53 |
Winters C, Heymans M W, van Wegen E E H, et al. How to design clinical rehabilitation trials for the upper paretic limb early post stroke? [J]. Trials, 2016, 17: 468.
|