Brain oscillations control hand orthosis in a tetraplegic
Abstract
The aim of the present study was to investigate whether self-induced brain potential changes could be useful as control signals for patients with severe motor impairment, i.e. due to high-level spinal cord injury. The pilot project was performed in a tetraplegic patient (T.S.), whose residual muscle activity of the upper limbs is restricted to the left biceps. To restore the hand grasp function, an electrical driven hand orthosis fitting his left hand was developed. The operation of this device is directly based on the bioelectrical signals of the brain. After some months of training, T. S. has learned to operate the hand orthosis by mental imagination of specific motor commands.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The extent and impact of subject learning effects in noninvasive MI BCI training remain rather disputable.
“Notwithstanding a few exceptions of longitudinal and translational studies in which thorough neuroimaging evidence is also provided [9,42], the extent and impact of subject learning effects in noninvasive MI BCI training remain rather disputable.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [9]
These works report on longitudinal training and involve end-users.
“These works report on longitudinal training and involve end-users.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [9]
Better BCI performance could be due to decoder recalibration, re-parameterizations of the BCI, and the application and adoption of better mental strategies, among other factors.
“Better performance could be due to decoder recalibration [29], re-parameterizations of the BCI, and the application and adoption of better mental strategies [9,64,65], among other factors.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [9]
Other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy or application performances.
“Secondly, other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy [9,27,30,31,33,35,51] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [9]
The most complete evidence of subject learning with obvious translational implications is offered in [9], [10], and [42].
“The most complete evidence of subject learning with obvious translational implications is offered in [9], [10], and [42].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [9]
No apparent "breakthroughs" are evident, which could support the only likely alternative hypothesis, that of the employment of better mental strategies sparking immediate, rather than gradual, improvements.
“No apparent “breakthroughs” are evident, which could support the only likely alternative hypothesis, that of the employment of better mental strategies sparking immediate, rather than gradual, improvements [9,64].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [9]
Cites
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577 · see citing works
Motor imagery activates primary sensorimotor area in humans
Pfurtscheller, G., & Neuper, C. (1997) · Neuroscience Letters · doi:10.1016/s0304-3940(97)00889-6 · Cited by 1,052 · see citing works
Self-citationModulation of Cortical Motor Output Maps During Development of Implicit and Explicit Knowledge
Pascual‐Leone, Á., Grafman, J. H., & Hallett, M. (1994) · Science · doi:10.1126/science.8122113 · Cited by 745 · see citing works
Pfurtscheller, G., Neuper, C., Schlögl, A., & Lugger, K. (1998) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.712230 · Cited by 553 · see citing works
Self-citationMultichannel EEG-based brain-computer communication
Wolpaw, J. R., & McFarland, D. J. (1994) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(94)90135-x · Cited by 449 · see citing works
A selective imaging of tinnitus
Giraud, A., Chéry-Croze, S., Fischer, G., Fischer, C., Vighetto, A., Grégoire, M.-C., Lavenne, F., & Collet, L. (1999) · Neuroreport · doi:10.1097/00001756-199901180-00001 · Cited by 341 · see citing works
Rapid prototyping of an EEG-based brain-computer interface (BCI)
Guger, C., Schlögl, A., Neuper, C., Walterspacher, D., Strein, T., & Pfurtscheller, G. (2001) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/7333.918276 · Cited by 312 · see citing works
Self-citationEnhancement of Left-Right Sensorimotor EEG Differences During Feedback-Regulated Motor Imagery
Neuper, C., Schlögl, A., & Pfurtscheller, G. (1999) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-199907000-00010 · Cited by 225 · see citing works
Self-citationChanges in motor cortical activity during visuomotor adaptation
Wise, S. P., Moody, S. L., Blomstrom, K. J., & Mitz, A. R. (1998) · Experimental Brain Research · doi:10.1007/s002210050462 · Cited by 180 · see citing works
EEG-based control of a hand grasp neuroprosthesis
Lauer, R. T., Peckham, P. H., & Kilgore, K. L. (1999) · Neuroreport · doi:10.1097/00001756-199906030-00026 · Cited by 106 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927 · see citing works
Self-citationBCI2000: A General-Purpose Brain-Computer Interface (BCI) System
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827072 · Cited by 3,022 · see citing works
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143 · see citing works
Motor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990 · see citing works
Self-citationBrain–computer interfaces for communication and rehabilitation
Chaudhary, U., Birbaumer, N., & Ramos-Murguialday, A. (2016) · Nature Reviews Neurology · doi:10.1038/nrneurol.2016.113 · Cited by 928 · see citing works
Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges
Millán, J. D. R. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00161 · Cited by 870 · see citing works
Brain-Computer Interfaces in Medicine
Shih, J. J., Krusienski, D. J., & Wolpaw, J. R. (2012) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2011.12.008 · Cited by 756 · see citing works
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
Lebedev, M., & Nicolelis, M. A. L. (2017) · Physiological Reviews · doi:10.1152/physrev.00027.2016 · Cited by 655 · see citing works
Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke
Buch, E. R., Weber, C., Cohen, L. G., Braun, C., Dimyan, M. A., Ard, T. D., Mellinger, J., Carìa, A., Soekadar, S. R., Fourkas, A. D., et al. (2008) · Stroke · doi:10.1161/strokeaha.107.505313 · Cited by 596 · see citing works
Pfurtscheller, G., Müller, G. R., Pfurtscheller, J., Gerner, H. J., & Rupp, R. (2003) · Neuroscience Letters · doi:10.1016/s0304-3940(03)00947-9 · Cited by 548 · see citing works
Self-citationHow many people are able to operate an EEG-based brain-computer interface (BCI)?
Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., & Pfurtscheller, G. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814481 · Cited by 526 · see citing works
Self-citationBrain-computer interfaces for communication and control
McFarland, D. J., & Wolpaw, J. R. (2011) · Communications of the ACM · doi:10.1145/1941487.1941506 · Cited by 523 · see citing works
Iturrate, I., Antelis, J. M., Kübler, A., & Mínguez, J. (2009) · IEEE Transactions on Robotics · doi:10.1109/tro.2009.2020347 · Cited by 515 · see citing works
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects
Mak, J. N., & Wolpaw, J. R. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2035356 · Cited by 513 · see citing works
Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks
Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B., & He, B. (2016) · Scientific Reports · doi:10.1038/srep38565 · Cited by 484 · see citing works
Leeb, R., Friedman, D. A., Muller-Putz, G. R., Scherer, R., Slater, M., & Pfurtscheller, G. (2007) · Computational Intelligence and Neuroscience · doi:10.1155/2007/79642 · Cited by 480 · see citing works
Self-citationEEG-based neuroprosthesis control: A step towards clinical practice
Müller-Putz, G. R., Scherer, R., Pfurtscheller, G., & Rupp, R. (2005) · Neuroscience Letters · doi:10.1016/j.neulet.2005.03.021 · Cited by 477 · see citing works
Self-citationAn MEG-based brain–computer interface (BCI)
Mellinger, J., Schalk, G., Braun, C., Preissl, H. T., Rosenstiel, W., Birbaumer, N., & Kübler, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.03.019 · Cited by 444 · see citing works
Kübler, A., & Birbaumer, N. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.06.019 · Cited by 441 · see citing works
Brain-Computer Interfaces Using Electrocorticographic Signals
Schalk, G., & Leuthardt, E. C. (2011) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2011.2172408 · Cited by 429 · see citing works
Neuper, C., Scherer, R., Wriessnegger, S. C., & Pfurtscheller, G. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.11.015 · Cited by 421 · see citing works
Self-citationBenabid, A. L., Costecalde, T., Eliseyev, A., Charvet, G., Verney, A., Karakas, S., Foerster, M., Lambert, A., Morinière, B., Abroug, N., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30321-7 · Cited by 404 · see citing works
Progress in Brain Computer Interface: Challenges and Opportunities
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.578875 · Cited by 404 · see citing works
EEG source imaging enhances the decoding of complex right-hand motor imagery tasks
Edelman, B. J., Baxter, B., & He, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2467312 · Cited by 370 · see citing works
Neuroergonomics: Research and practice
Parasuraman, R. (2003) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220210199753 · Cited by 346 · see citing works
Regulation of anterior insular cortex activity using real-time fMRI
Carìa, A., Veit, R., Sitaram, R., Lotze, M., Weiskopf, N., Grodd, W., & Birbaumer, N. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.01.018 · Cited by 343 · see citing works
Lazarou, I., Nikolopoulos, S., Petrantonakis, P. C., Kompatsiaris, I. Y., & Tsolaki, M. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00014 · Cited by 340 · see citing works
Nijboer (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00055 · Cited by 323 · see citing works
Neuper, C., Müller, G. R., Kübler, A., Birbaumer, N., & Pfurtscheller, G. (2003) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00387-5 · Cited by 317 · see citing works
Self-citationTrends in EEG-BCI for daily-life: Requirements for artifact removal
Minguillón, J., López-Gordo, M. A., & Pelayo, F. J. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.09.005 · Cited by 291 · see citing works
Decoding flexion of individual fingers using electrocorticographic signals in humans
Kubanek, J., Miller, K. J., Ojemann, J. G., Wolpaw, J. R., & Schalk, G. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/6/066001 · Cited by 286 · see citing works
Graz-BCI: state of the art and clinical applications
Pfurtscheller, G., Neuper, C., Müller, G., Obermaier, B., Krausz, G., Schlögl, A., Scherer, R., Graimann, B., Keinrath, C., Skliris, D., et al. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814454 · Cited by 285 · see citing works
Self-citationAn SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia
Ortner, R., Allison, B. Z., Korisek, G., Gaggl, H., & Pfurtscheller, G. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2010.2076364 · Cited by 281 · see citing works
Self-citationBrain–computer interface in paralysis
Birbaumer, N., Ramos‐Murguialday, A., & Cohen, L. G. (2008) · Current Opinion in Neurology · doi:10.1097/wco.0b013e328315ee2d · Cited by 276 · see citing works
Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI)
Weiskopf, N., Scharnowski, F., Veit, R., Goebel, R., Birbaumer, N., & Mathiak, K. (2004) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2005.09.019 · Cited by 270 · see citing works
Upper limb movements can be decoded from the time-domain of low-frequency EEG
Ofner, P., Schwarz, A., Pereira, J., & Müller-Putz, G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182578 · Cited by 241 · see citing works
Decoding natural grasp types from human ECoG
Pistohl, T., Schulze‐Bonhage, A., Aertsen, A., Mehring, C., & Ball, T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.084 · Cited by 249 · see citing works
Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms
He, B., Baxter, B., Edelman, B. J., Cline, C. C., & Ye, W. W. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2407272 · Cited by 233 · see citing works
Rehabilitation with Brain-Computer Interface Systems
Pfurtscheller, G., Muller-Putz, G. R., Scherer, R., & Neuper, C. (2008) · Computer · doi:10.1109/mc.2008.432 · Cited by 220 · see citing works
Self-citationHramov, A. E., Maksimenko, V., & Pisarchik, A. N. (2021) · Physics Reports · doi:10.1016/j.physrep.2021.03.002 · Cited by 218 · see citing works
Brain-computer interface technologies: from signal to action
Ortiz-Rosario, A., & Adeli, H. (2013) · Reviews in the Neurosciences · doi:10.1515/revneuro-2013-0032 · Cited by 212 · see citing works
Motor imagery and EEG-based control of spelling devices and neuroprostheses
Neuper, C., Muller-Putz, G. R., Scherer, R., & Pfurtscheller, G. (2006) · Progress in brain research · doi:10.1016/s0079-6123(06)59025-9 · Cited by 209 · see citing works
Self-citationBrain-Computer Interface Operation of Robotic and Prosthetic Devices
McFarland, D. J., & Wolpaw, J. R. (2008) · Computer · doi:10.1109/mc.2008.409 · Cited by 201 · see citing works
Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard
Uşaklı, A. B., & Gürkan, S. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2009.2030923 · Cited by 198 · see citing works
Real-world applications for brain-computer interface technology
Moore, M. M. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814433 · Cited by 198 · see citing works
"Virtual keyboard" controlled by spontaneous EEG activity
Obermaier, B., Müller, G., & Pfurtscheller, G. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.816866 · Cited by 191 · see citing works
Self-citationNon-Invasive Brain-Computer Interfaces: State of the Art and Trends
Edelman, B., Zhang, S.-L., Schalk, G., Brunner, P., Müller-Putz, G., Guan, C.-T., & He, B. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3449790 · Cited by 188 · see citing works
Schultz, A. E., & Kuiken, T. A. (2011) · PM&R · doi:10.1016/j.pmrj.2010.06.016 · Cited by 186 · see citing works
Rohm, M., Schneiders, M., Müller, C., Kreilinger, A., Kaiser, V., Muller-Putz, G. R., & Rupp, R. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.07.004 · Cited by 185 · see citing works
EEG feature comparison and classification of simple and compound limb motor imagery
Yi, W., Qiu, S., Qi, H., Zhang, L., Wan, B., & Ming, D. (2013) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-10-106 · Cited by 181 · see citing works
Brain-Computer Interfacing: An Introduction
Rao, R. P. N. (2019) · Cited by 181 · see citing works
Could the beta rebound in the EEG be suitable to realize a “brain switch”?
Pfurtscheller, G., & Solis‐Escalante, T. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.09.027 · Cited by 170 · see citing works
Self-citationOnose, G., Grozea, C., Anghelescu, A., Daia, C. O., Sinescu, C. J., Ciurea, A. V., Spircu, T., Mirea, A., Andone, I., Spînu, A., et al. (2012) · Spinal Cord · doi:10.1038/sc.2012.14 · Cited by 169 · see citing works
Schwemmer, M. A., Skomrock, N. D., Sederberg, P. B., Ting, J. E., Sharma, G., Bockbrader, M. A., & Friedenberg, D. A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0171-y · Cited by 167 · see citing works
The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users
Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millán, J. D. R. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2003787 · Cited by 166 · see citing works
Moghimi, S., Kushki, A., Guerguerian, A. M., & Chau, T. T. (2012) · Assistive Technology · doi:10.1080/10400435.2012.723298 · Cited by 166 · see citing works
Ofner, P., Schwarz, A., Pereira, J., Wyss, D., Wildburger, R., & Müller-Putz, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-43594-9 · Cited by 165 · see citing works
Feature learning from incomplete EEG with denoising autoencoder
Li, J., Struzik, Z. R., Zhang, L., & Cichocki, A. S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.08.092 · Cited by 165 · see citing works
Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm
Chen, X., Zhao, B., Wang, Y., & Gao, X. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaf594 · Cited by 160 · see citing works
Müller, G., Neuper, C., Rupp, R., Keinrath, C., Gerner, H. J., & Pfurtscheller, G. (2003) · Neuroscience Letters · doi:10.1016/s0304-3940(03)00019-3 · Cited by 159 · see citing works
Self-citationDetection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks
Trigili, E., Grazi, L., Crea, S., Accogli, A., Carpaneto, J., Micera, S., Vitiello, N., & Panarese, A. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0512-1 · Cited by 155 · see citing works
Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand
Acharya, S., Fifer, M. S., Benz, H. L., Crone, N. E., & Thakor, N. V. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/4/046002 · Cited by 151 · see citing works
Chapter 8 Neurofeedback and Brain–Computer Interface
Birbaumer, N., Ramos‐Murguialday, A., Weber, C., & Montoya, P. (2009) · International review of neurobiology · doi:10.1016/s0074-7742(09)86008-x · Cited by 151 · see citing works
Non-invasive control interfaces for intention detection in active movement-assistive devices
Lobo-Prat, J., Kooren, P. N., Stienen, A. H. A., Herder, J. L., Koopman, H. F. J. M., & Veltink, P. H. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-168 · Cited by 150 · see citing works
Information conveyed through brain-control: cursor versus robot
Taylor, D. M., Tillery, S. I. H., & Schwartz, A. B. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814451 · Cited by 149 · see citing works
A research agenda for physiological computing
Allanson, J., & Fairclough, S. H. (2004) · Interacting with Computers · doi:10.1016/j.intcom.2004.08.001 · Cited by 144 · see citing works
Eye-gaze independent EEG-based brain–computer interfaces for communication
Riccio, A., Donatella Mattia, A., Simione, L., Belardinelli, M. O., & Febo Cincotti, A. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/4/045001 · Cited by 140 · see citing works
Evolution of brain-computer interfaces: going beyond classic motor physiology
Leuthardt, E. C., Schalk, G., Roland, J. L., Rouse, A. G., & Moran, D. W. (2009) · Neurosurgical FOCUS · doi:10.3171/2009.4.focus0979 · Cited by 135 · see citing works
Chestek, C. A., Gilja, V., Blabe, C. H., Foster, B. L., Shenoy, K. V., Parvizi, J., & Henderson, J. M. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/2/026002 · Cited by 135 · see citing works
EEG Classification of Different Imaginary Movements within the Same Limb
Yong, X., & Menon, C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121896 · Cited by 133 · see citing works
Brain-machine interfaces for rehabilitation in stroke: A review
López‐Larraz, E., Sarasola-Sanz, A., Irastorza-Landa, N., Birbaumer, N., & Ramos‐Murguialday, A. (2018) · Neurorehabilitation · doi:10.3233/nre-172394 · Cited by 132 · see citing works
The Emerging World Of Motor Neuroprosthetics: a Neurosurgical Perspective
Leuthardt, E. C., Schalk, G., Moran, D. W., & Ojemann, J. G. (2006) · Neurosurgery · doi:10.1227/01.neu.0000221506.06947.ac · Cited by 128 · see citing works
The effect of distinct mental strategies on classification performance for brain–computer interfaces
Friedrich, E. V. C., Scherer, R., & Neuper, C. (2012) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2012.01.014 · Cited by 125 · see citing works
Self-citationMachado, S. E. D. C., Araújo, F., Paes, F., Velasques, B., Cunha, M., Budde, H., Basile, L., Anghinah, R., Arias-Carrión, Ó., Cagy, M., et al. (2010) · Reviews in the Neurosciences · doi:10.1515/revneuro.2010.21.6.451 · Cited by 125 · see citing works
Sorger, B., Dahmen, B., Reithler, J., Gosseries, O., Maudoux, A., Laureys, S., & Goebel, R. (2009) · Progress in brain research · doi:10.1016/s0079-6123(09)17719-1 · Cited by 122 · see citing works
EEG-Based Asynchronous BCI Controls Functional Electrical Stimulation in a Tetraplegic Patient
Pfurtscheller, G., Muller-Putz, G. R., Pfurtscheller, J., & Rupp, R. (2005) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/asp.2005.3152 · Cited by 122 · see citing works
Self-citationFast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG
Muller-Putz, G. R., Kaiser, V., Solis‐Escalante, T., & Pfurtscheller, G. (2010) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-009-0572-7 · Cited by 121 · see citing works
Self-citationThe Development of Brain-Machine Interface Neuroprosthetic Devices
Patil, P. G., & Turner, D. A. (2008) · Neurotherapeutics · doi:10.1016/j.nurt.2007.11.002 · Cited by 118 · see citing works
Cruse, D., Chennu, S., Fernández‐Espejo, D., Payne, W. L., Young, G. B., & Owen, A. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049933 · Cited by 116 · see citing works
EEG-based classification of imaginary left and right foot movements using beta rebound
Hashimoto, Y., & Ushiba, J. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.05.006 · Cited by 116 · see citing works
Sensors for brain-computer interfaces
Hochberg, L. R., & Donoghue, J. P. (2006) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2006.1705745 · Cited by 116 · see citing works
Vučković, A., & Osuagwu, B. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.02.016 · Cited by 112 · see citing works
Brain-Machine Interface (BMI) in paralysis
Chaudhary, U., Birbaumer, N., & Curado, M. R. (2015) · Annals of Physical and Rehabilitation Medicine · doi:10.1016/j.rehab.2014.11.002 · Cited by 112 · see citing works
Most Popular Signal Processing Methods in Motor-Imagery BCI: A Review and Meta-Analysis
Wierzgała, P., Zapała, D., Wójcik, G. M., & Masiak, J. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00078 · Cited by 112 · see citing works
Brain Computer Interfaces in Rehabilitation Medicine
Bockbrader, M. A., Francisco, G. E., Lee, R., Olson, J. D., Solinsky, R. J., & Boninger, M. L. (2018) · PM&R · doi:10.1016/j.pmrj.2018.05.028 · Cited by 110 · see citing works
Ono, T., Kimura, A., & Ushiba, J. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.03.006 · Cited by 108 · see citing works
A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control
Tang, Z., Sun, S., Zhang, S., Chen, Y., Li, C., & Chen, S. (2016) · Sensors · doi:10.3390/s16122050 · Cited by 106 · see citing works
Neural machine interfaces for controlling multifunctional powered upper-limb prostheses
Ohnishi, K., Weir, R. F. F., & Kuiken, T. (2006) · Expert Review of Medical Devices · doi:10.1586/17434440.4.1.43 · Cited by 103 · see citing works
Cantillo-Negrete, J., Carino-Escobar, R. I., Carrillo-Mora, P. U., Elı́as-Viñas, D., & Gutiérrez-Martínez, J. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/1624637 · Cited by 102 · see citing works
Use of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI
Sakurada, T., Kawase, T., Komatsu, T., & Kansaku, K. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.12.010 · Cited by 102 · see citing works
Enzinger, C., Ropele, S., Fazekas, F., Loitfelder, M., Gorani, F., Seifert‐Held, T., Reiter, G., Neuper, C., Pfurtscheller, G., & Muller-Putz, G. R. (2008) · Experimental Brain Research · doi:10.1007/s00221-008-1465-y · Cited by 102 · see citing works
Self-citationMuller-Putz, G. R., Scherer, R., Pfurtscheller, G., & Rupp, R. (2006) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt.2006.011 · Cited by 101 · see citing works
Self-citationBrain–computer interfaces (BCIs): Detection instead of classification
Schalk, G., Brunner, P., Gerhardt, L. A., Bischof, H., & Wolpaw, J. R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.08.010 · Cited by 100 · see citing works
Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles!
Leeb, R., Keinrath, C., Friedman, D. A., Guger, C., Scherer, R., Neuper, C., Garau, M., Antley, A., Steed, A. J., Slater, M., et al. (2006) · PRESENCE Virtual and Augmented Reality · doi:10.1162/pres.15.5.500 · Cited by 99 · see citing works
Self-citationA Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback
Fleury, M., Lioi, G., Barillot, C., & Lécuyer, A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00528 · Cited by 98 · see citing works
A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements
Kawase, T., Sakurada, T., Koike, Y., & Kansaku, K. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa525f · Cited by 94 · see citing works
EEG and MEG brain-computer interface for tetraplegic patients
Kauhanen, L., Nykopp, T., Lehtonen, J., Jylänki, P., Heikkonen, J., Rantanen, P., Alaranta, H., & Sams, M. E. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875546 · Cited by 94 · see citing works
Kosmyna, N., Lindgren, J. T., & Lécuyer, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-31472-9 · Cited by 92 · see citing works
Thinking Penguin: Multimodal Brain–Computer Interface Control of a VR Game
Leeb, R., Lancelle, M., Kaiser, V., Fellner, D. W., & Pfurtscheller, G. (2013) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2013.2242072 · Cited by 91 · see citing works
Self-citationBrain-computer interfaces — the key for the conscious brain locked into a paralyzed body
Kübler, A., & Neumann, N. (2005) · Progress in brain research · doi:10.1016/s0079-6123(05)50035-9 · Cited by 90 · see citing works
- Authors
- Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., & Neuper, C.
- Affiliations
- Graz University of Technology (AT)
- Corresponding author
- Pfurtscheller, G
- ORCID
- Neuper, C
- Venue
- Neuroscience Letters
- Publisher
- Elsevier BV
- Published
- 2000-10
- Volume
- 292
- Issue
- 3
- Pages
- 211-214
- ISSN
- 0304-3940 (ISSN-L)
- OpenAlex
- W2004092686
- Cited by
- 472
- References
- 10
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 14 · 2023: 13 · 2022: 15 · 2021: 22 · 2020: 17 · 2019: 18 · 2018: 25 · 2017: 25 · 2016: 24 · 2015: 29 · 2014: 28 · 2013: 32 · 2012: 22
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neuroscience Letters (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- neural oscillations, hand orthoses, tetraplegia, high spinal cord injury, motor imagery
- MeSH terms
- Adult, Brain, Electroencephalography, Hand, Humans, Male, Orthotic Devices, Pilot Projects, Quadriplegia, Robotics, Self-Help Devices, Signal Processing, Computer-Assisted, Treatment Outcome, Hand Strength
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
