References
- R. Bene, N. Beck, B. Vajda, S. Popovic, K. Cosic, and V. Demarin, "Interface providers in stroke neurorehabilitation," Periodicum Biologorum, vol. 114, no. 3, pp. 403-407, 2012.
- V. L. Feigin, C. M. M. Lawes, D. A. Bennett, S. L. Barker-Collo, and V. Parag, "Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review," Lancet Neurology, vol. 8, no. 4, pp. 355-369, 2009. https://doi.org/10.1016/S1474-4422(09)70025-0
- P. W. Duncan, L. B. Goldstein, D. Matchar, G. W. Divine, and J. Feussner, "Measurement of motor recovery after stroke: outcome assessment and sample size requirements," Stroke, vol. 23, no. 8, pp. 1084-1089, 1992. https://doi.org/10.1161/01.STR.23.8.1084
- C. Calautti and J. C. Baron, "Functional neuroimaging studies of motor recovery after stroke in adults: a review," Stroke, vol. 34, no. 6, pp. 1553-1566, 2003. https://doi.org/10.1161/01.STR.0000071761.36075.A6
- J. C. Grotta, E. A. Noser, T. Ro, C. Boake, H. Levin, J. Aronowski, and T. Schallert, "Constraint-induced movement therapy," Stroke, vol. 35, no. 11 (suppl 1), pp. 2699-2701, 2004. https://doi.org/10.1161/01.STR.0000143320.64953.c4
- N. Sharma, V. M. Pomeroy, and J. C. Baron, "Motor imagery: a backdoor to the motor system after stroke?," Stroke, vol. 37, no. 7, pp. 1941-1952, 2006. https://doi.org/10.1161/01.STR.0000226902.43357.fc
- A. P. Georgopoulos, J. T. Lurito, M. Petrides, A. B. Schwartz, and J. T. Massey, "Mental rotation of the neuronal population vector," Science, vol. 243, no. 4888, pp. 234-236, 1989. https://doi.org/10.1126/science.2911737
- N. Sharma, L. H. Simmons, P. S. Jones, D. J. Day, T. A. Carpenter, V. M. Pomeroy, E. A. Warburton, and J. C. Baron, "Motor imagery after subcortical stroke: a functional magnetic resonance imaging study," Stroke, vol. 40, no. 4, pp. 1315-1324, 2009. https://doi.org/10.1161/STROKEAHA.108.525766
- S. H. Johnson, "Imagining the impossible: intact motor representations in hemiplegics," NeuroReport, vol. 11, no. 4, pp. 729-732, 2000. https://doi.org/10.1097/00001756-200003200-00015
- S. H. Johnson, G. Sprehn, and A. J. Saykin, "Intact motor imagery in chronic upper limb hemiplegics: evidence for activity-independent action representations," Journal of Cognitive Neuroscience, vol. 14, no. 6, pp. 841-852, 2002. https://doi.org/10.1162/089892902760191072
- J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, no. 6, pp. 767-791, 2002. https://doi.org/10.1016/S1388-2457(02)00057-3
- J. J. Daly and J. R. Wolpaw, "Brain-computer interfaces in neurological rehabilitation," Lancet Neurology, vol. 7, no. 11, pp. 1032-1043, 2008. https://doi.org/10.1016/S1474-4422(08)70223-0
- J. J. Shih, D. J. Krusienski, and J. R. Wolpaw, "Brain-computer interfaces in medicine," Mayo Clinic Proceedings, vol. 87, no. 3, pp. 268-279, 2012. https://doi.org/10.1016/j.mayocp.2011.12.008
- P. L. Jackson, M. F. Lafleur, F. Malouin, C. Richards, and J. Doyon, "Potential role of mental practice using motor imagery in neurologic rehabilitation," Archives of Physical Medicine and Rehabilitation, vol. 82, no. 8, pp. 1133-1141, 2001. https://doi.org/10.1053/apmr.2001.24286
- L. F. Nicolas-Alonso and J. Gomez-Gil, "Brain computer interfaces: a review," Sensors, vol. 12, no. 2, pp. 1211-1279, 2012. https://doi.org/10.3390/s120201211
- S. Silvoni, A. Ramos-Murguialday, M. Cavinato, C. Volpato, G. Cisotto, A. Turolla, F. Piccione, and N. Birbaumer, "Brain-computer interface in stroke: a review of progress," Clinical EEG and Neuroscience, vol. 42, no. 4, pp. 245-252, 2011. https://doi.org/10.1177/155005941104200410
- S. Bonnie and R. Martin, "Understanding controlled trials: Why are randomised controlled trials important?," British Medical Journal, vol. 316, no. 7126, p. 201, 1998. https://doi.org/10.1136/bmj.316.7126.201
- G. Pfurtscheller, G. R. Muller, J. Pfurtscheller, H. J. Gerner, and R. Rupp, "'Thought': control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia," Neuroscience Letters, vol. 351, no. 1, pp. 33-36, 2003. https://doi.org/10.1016/S0304-3940(03)00947-9
- A. Mohapp, R. Scherer, C. Keinrath, P. Grieshofer, G. Pfurtscheller, and C. Neuper, "Single-trial EEG classification of executed and imagined hand movements in hemiparetic stroke patients," in Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course, Graz, Austria, 2006, pp. 80-81.
- O. Bai, P. Lin, S. Vorbach, M. K. Floeter, N. Hattori, and M. Hallett, "A high performance sensorimotor beta rhythmbased brain-computer interface associated with human natural motor behavior," Journal of Neural Engineering, vol. 5, no. 1, pp. 24-35, 2008. https://doi.org/10.1088/1741-2560/5/1/003
- A. Biasiucci, R. Chavarriaga, B. Hamner, R. Leeb, F. Pichiorri, F. De Vico Fallani, D. Mattia, and J. R. del Millan, "Combining discriminant and topographic information in BCI: preliminary results on stroke patients," in Proceedings of the 5th International IEEE/EMBS Conference on Neural Engineering, Cancun, Mexico, 2011, pp. 290-293.
- M. Gomez-Rodriguez, J. Peters, J. Hill, B. Schölkopf, A. Gharabaghi, and M. Grosse-Wentrup, "Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery," Journal of Neural Engineering, vol. 8, no. 3, p. 036005, 2011. https://doi.org/10.1088/1741-2560/8/3/036005
- I. K. Niazi, N. Jiang, O. Tiberghien, J. F. Nielsen, K. Dremstrup, and D. Farina, "Detection of movement intention from single-trial movement-related cortical potentials," Journal of Neural Engineering, vol. 8, no. 6, p. 066009, 2011. https://doi.org/10.1088/1741-2560/8/6/066009
- W. K. Tam, K. Y. Tong, F. Meng, and S. K. Gao, "A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-session study," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 6, pp. 617-627, 2011. https://doi.org/10.1109/TNSRE.2011.2168542
- M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, "Robust EEG channel selection across sessions in brain-computer interface involving stroke patients," in Proceedings of the IEEE International Joint Conference on Neural Networks, Brisbane, Australia, 2012, pp. 2319-2324.
- M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, "Omitting the intra-session calibration in EEG-based brain computer interface used for stroke rehabilitation," in Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, 2012, pp. 4124-4127.
- D. T. Bundy, M. Wronkiewicz, M. Sharma, D. W. Moran, M. Corbetta, and E. C. Leuthardt, "Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors," Journal of Neural Engineering, vol. 9, no. 3, p. 036011, 2012. https://doi.org/10.1088/1741-2560/9/3/036011
- F. Cincotti, F. Pichiorri, P. Arico, F. Aloise, F. Leotta, F. D. Fallani, J. D. Millan, M. Molinari, and D. Mattia, "EEGbased brain-computer interface to support post-stroke motor rehabilitation of the upper limb," in Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, 2012, pp. 4112-4115.
- A. Frisoli, C. Loconsole, D. Leonardis, F. Banno, M. Barsotti, C. Chisari, and M. Bergamasco, "A new gaze-BCIdriven control of an upper limb exoskeleton for rehabilitation in real-world tasks," IEEE Transactions on Systems Man and Cybernetics Part C, vol. 42, no. 6, pp. 1169-1179, 2012. https://doi.org/10.1109/TSMCC.2012.2226444
- V. Kaiser, I. Daly, F. Pichiorri, D. Mattia, G. R. Muller-Putz, and C. Neuper, "Relationship between electrical brain responses to motor imagery and motor impairment in stroke," Stroke, vol. 43, no. 10, pp. 2735-2740, 2012. https://doi.org/10.1161/STROKEAHA.112.665489
- M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, "Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain-computer interface,"IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 4, pp. 610-619, 2013. https://doi.org/10.1109/TNNLS.2013.2239310
- M. Naeem, C. Brunner, R. Leeb, B. Graimann, and G. Pfurtscheller, "Seperability of four-class motor imagery data using independent components analysis," Journal of Neural Engineering, vol. 3, no. 3, pp. 208-216, 2006. https://doi.org/10.1088/1741-2560/3/3/003
- E. Buch, C. Weber, L. G. Cohen, C. Braun, M. A. Dimyan, T. Ard, J. Mellinger, A. Caria, S. Soekadar, A. Fourkas, and N. Birbaumer, "Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke," Stroke, vol. 39, no. 3, pp. 910-917, 2008. https://doi.org/10.1161/STROKEAHA.107.505313
- K. K. Ang, C. Guan, K. S. G. Chua, B. T. Ang, C. W. K. Kuah, C. Wang, K. S. Phua, Z. Y. Chin, and H. Zhang, "A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation," in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, 2009, pp. 5981-5984.
- A. R. Fugl-Meyer, L. Jaasko, I. Leyman, S. Olsson, and S. Steglind, "The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance," Scandinavian Journal of Rehabilitation Medicine, vol. 7, no. 1, pp. 13-31, 1975.
- J. J. Daly, R. Cheng, J. Rogers, K. Litinas, K. Hrovat, and M. Dohring, "Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke," Journal of Neurologic Physical Therapy, vol. 33, no. 4, pp. 203-211, 2009. https://doi.org/10.1097/NPT.0b013e3181c1fc0b
- K. K. Ang, C. Guan, K. S. G. Chua, B. T. Ang, C. W. K. Kuah, C. Wang, K. S. Phua, Z. Y. Chin, and H. Zhang, "Clinical study of neurorehabilitation in stroke using EEGbased motor imagery brain-computer interface with robotic feedback," in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, 2010, pp. 5549-5552.
- D. Broetz, C. Braun, C. Weber, S. R. Soekadar, A. Caria, and N. Birbaumer, "Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report," Neurorehabilitation and Neural Repair, vol. 24, no. 7, pp. 674-679, 2010. https://doi.org/10.1177/1545968310368683
- G. Prasad, P. Herman, D. Coyle, S. McDonough, and J. Crosbie, "Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study," Journal of Neuroengineering and Rehabilitation, vol. 7, no. 1, p. 60, 2010. https://doi.org/10.1186/1743-0003-7-60
- K. K. Ang, C. Guan, K. S. G. Chua, B. T. Ang, C. W. K. Kuah, C. Wang, K. S. Phua, Z. Y. Chin, and H. Zhang, "A large clinical study on the ability of stroke patients to use EEG-based motor imagery brain-computer interface," Clinical EEG and Neuroscience, vol. 42, no. 4, pp. 253-258, 2011. https://doi.org/10.1177/155005941104200411
- K. Shindo, K. Kawashima, J. Ushiba, N. Ota, M. Ito, T. Ota, A. Kimura, and M. G. Liu, "Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study," Journal of Rehabilitation Medicine, vol. 43, no. 10, pp. 951-957, 2011. https://doi.org/10.2340/16501977-0859
- H. Y. Sun, Y. Xiang, and M. D. Yang, "Neurological rehabilitation of stroke patients via motor imaginary-based braincomputer interface technology," Neural Regeneration Research, vol. 6, no. 28, pp. 2198-2202, 2011.
- K. K. Ang, C. Guan, K. S. Phua, C. Wang, I. Teh, C. W. Chen, and E. Chew, "Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation," in Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, 2012, pp. 4128-4131.
- E. R. Buch, A. M. Shanechi, A. D. Fourkas, C. Weber, N. Birbaumer, and L. G. Cohen, "Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke," Brain, vol. 135, pp. 596-614, 2012. https://doi.org/10.1093/brain/awr331
- Y. Kasashima, T. Fujiwara, Y. Matsushika, T. Tsuji, K. Hase, J. Ushiyama, J. Ushiba, and M. G. Liu, "Modulation of event-related desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke," Experimental Brain Research, vol. 221, no. 3, pp. 263-268, 2012. https://doi.org/10.1007/s00221-012-3166-9
- M. Takahashi, K. Takeda, Y. Otaka, R. Osu, T. Hanakawa, M. Gouko, and K. Ito, "Event related desynchronizationmodulated functional electrical stimulation system for stroke rehabilitation: a feasibility study," Journal of Neuroengineering and Rehabilitation, vol. 9, p. 56, 2012. https://doi.org/10.1186/1743-0003-9-56
- M. Mihara, N. Hattori, M. Hatakenaka, H. Yagura, T. Kawano, T. Hino, and I. Miyai, "Near-infrared spectroscopymediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study," Stroke, vol. 44, no. 4, pp. 1091-1098, 2013. https://doi.org/10.1161/STROKEAHA.111.674507
- B. Varkuti, C. Guan, Y. Pan, K. S. Phua, K. K. Ang, C. W. K. Kuah, K. Chua, B. Ti Ang, N. Birbaumer, and R. Sitaram, "Resting state changes in functional connectivity correlate With movement recovery for BCI and robot-assisted upper-extremity training after stroke," Neurorehabilitation and Neural Repair, vol. 27, no. 1, pp. 53-62, 2013. https://doi.org/10.1177/1545968312445910
- A. Caria, C. Weber, D. Brotz, A. Ramos, L. F. Ticini, A. Gharabaghi, C. Braun, and N. Birbaumer, "Chronic stroke recovery after combined BCI training and physiotherapy: a case report," Psychophysiology, vol. 48, no. 4, pp. 578-582, 2011. https://doi.org/10.1111/j.1469-8986.2010.01117.x
Cited by
- Improving motor imagery BCI with user response to feedback vol.4, pp.1-2, 2017, https://doi.org/10.1080/2326263X.2017.1303253
- Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent vol.11, 2017, https://doi.org/10.3389/fninf.2017.00045
- Therapeutic applications of BCI technologies vol.4, pp.1-2, 2017, https://doi.org/10.1080/2326263X.2017.1307625
- Assessment of Cognitive Engagement in Stroke Patients From Single-Trial EEG During Motor Rehabilitation 2014, https://doi.org/10.1109/TNSRE.2014.2356472
- A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke vol.13, pp.5, 2016, https://doi.org/10.1080/17434440.2016.1174572
- 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 vol.23, pp.1, 2016, https://doi.org/10.1002/oti.1403
- A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography vol.2014, 2014, https://doi.org/10.1155/2014/176857
- Feasibility of a Hybrid Brain-Computer Interface for Advanced Functional Electrical Therapy vol.2014, 2014, https://doi.org/10.1155/2014/797128
- Effects of training pre-movement sensorimotor rhythms on behavioral performance vol.12, pp.6, 2015, https://doi.org/10.1088/1741-2560/12/6/066021
- Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters vol.9, pp.2, 2014, https://doi.org/10.1371/journal.pone.0087056
- Low-Rank Linear Dynamical Systems for Motor Imagery EEG vol.2016, 2016, https://doi.org/10.1155/2016/2637603
- Active Data Selection for Motor Imagery EEG Classification vol.62, pp.2, 2015, https://doi.org/10.1109/TBME.2014.2358536
- Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces vol.103, pp.6, 2015, https://doi.org/10.1109/JPROC.2015.2404941
- Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review vol.9, pp.9, 2017, https://doi.org/10.1016/j.pmrj.2017.04.016
- Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns vol.10, pp.12, 2015, https://doi.org/10.1371/journal.pone.0143962
- EEG Classification of Different Imaginary Movements within the Same Limb vol.10, pp.4, 2015, https://doi.org/10.1371/journal.pone.0121896
- Noninvasive Brain-Computer Interface: Decoding Arm Movement Kinematics and Motor Control vol.2, pp.4, 2016, https://doi.org/10.1109/MSMC.2016.2576638
- A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study vol.14, pp.1, 2017, https://doi.org/10.1186/s12984-017-0307-1
- Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke vol.103, pp.6, 2015, https://doi.org/10.1109/JPROC.2015.2415800
- EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation vol.25, pp.4, 2017, https://doi.org/10.1109/TNSRE.2016.2646763
- Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG vol.12, pp.5, 2015, https://doi.org/10.1088/1741-2560/12/5/056013
- Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation vol.54, pp.10, 2016, https://doi.org/10.1007/s11517-015-1421-5
- Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke vol.7, 2014, https://doi.org/10.3389/fneng.2014.00030
- Investigating the impact of feedback update interval on the efficacy of restorative brain–computer interfaces vol.4, pp.8, 2017, https://doi.org/10.1098/rsos.170660
- Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task vol.10, 2016, https://doi.org/10.3389/fnhum.2016.00170
- Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces vol.29, pp.6, 2017, https://doi.org/10.1162/NECO_a_00963
- Cortical excitability correlates with the event-related desynchronization during brain–computer interface control vol.15, pp.2, 2018, https://doi.org/10.1088/1741-2552/aa9c8c
- Effects of Continuous Kinaesthetic Feedback Based on Tendon Vibration on Motor Imagery BCI Performance vol.26, pp.1, 2018, https://doi.org/10.1109/TNSRE.2017.2739244
- Behavioral Outcomes Following Brain–Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial vol.12, pp.1662-453X, 2018, https://doi.org/10.3389/fnins.2018.00752
- Investigation of Optimal Afferent Feedback Modality for Inducing Neural Plasticity with A Self-Paced Brain-Computer Interface vol.18, pp.11, 2018, https://doi.org/10.3390/s18113761
- Genetic-based feature selection for efficient motion imaging of a brain–computer interface framework vol.15, pp.5, 2018, https://doi.org/10.1088/1741-2552/aad567
- A review: Motor rehabilitation after stroke with control based on human intent vol.232, pp.4, 2018, https://doi.org/10.1177/0954411918755828
- A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update vol.15, pp.3, 2018, https://doi.org/10.1088/1741-2552/aab2f2
- Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review pp.13582267, 2019, https://doi.org/10.1002/pri.1764
- A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity vol.14, pp.1, 2019, https://doi.org/10.1371/journal.pone.0207351