Brain oscillations control hand orthosis in a tetraplegic

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

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

Modulation 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

Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters

Pfurtscheller, G., Neuper, C., Schlögl, A., & Lugger, K. (1998) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.712230 · Cited by 553

Multichannel 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

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

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

Enhancement 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

Changes 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 184

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

Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects

Keihani, A., Shirzhiyan, Z., Farahi, M., Shamsi, E., Mahnam, A., Makkiabadi, B., Haidari, M. R., & Jafari‬, A. H. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00201 · Cited by 27

Brain Signals for Brain–Computer Interfaces

Wolpaw, J. R., & Boulay, C. (2009) · The frontiers collection · doi:10.1007/978-3-642-02091-9_2 · Cited by 27

Decoding Motor Signals From the Pediatric Cortex: Implications for Brain-Computer Interfaces in Children

Breshears, J. D., Gaona, C. M., Roland, J. L., Sharma, M., Anderson, N., Bundy, D. T., Freudenburg, Z. V., Smyth, M. D., Zempel, J. M., Limbrick, D. D., et al. (2011) · PEDIATRICS · doi:10.1542/peds.2010-1519 · Cited by 29

Identification of arm movements using correlation of electrocorticographic spectral components and kinematic recordings

Márquez-Chin, C., Popović, M. R., Thrasher, T. A., Cameron, T., Lozano, A. M., & Chen, R. E. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/014 · Cited by 27

Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot

Lisi, G., Noda, T., & Morimoto, J. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00085 · Cited by 27

Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography

Gibson, R. M., Chennu, S., Owen, A. M., & Cruse, D. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.11.034 · Cited by 26

Epidural electrocorticography of phantom hand movement following long-term upper-limb amputation

Gharabaghi, A., Naros, G., Walter, A., Roth, A., Bogdan, M., Rosenstiel, W., Mehring, C., & Birbaumer, N. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00285 · Cited by 25

Prospect of brain-machine interface in motor disabilities: The future support for multiple sclerosis patient to improve quality of life

Singh, A., Zamboni, P., & Khare, M. (2014) · Annals of Medical and Health Sciences Research · doi:10.4103/2141-9248.133447 · Cited by 25

Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals

Ribeiro, P. R. D. A., Brasil, F. L., Witkowski, M., Shiman, F., Cipriani, C., Vitiello, N., Carrozza, M. C., & Soekadar, S. R. (2013) · Advances in Human-Computer Interaction · doi:10.1155/2013/369425 · Cited by 24

Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

Quiles, E., Suay, F., Candela, G., Chio, N., Jiménez, M., & Álvarez-Kurogi, L. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17030699 · Cited by 24

Synergistic control of forearm based on accelerometer data and artificial neural networks

Mijović, B., Popović, M. B., & Popović, D. B. (2008) · Brazilian Journal of Medical and Biological Research · doi:10.1590/s0100-879x2008005000019 · Cited by 24

Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing

Mondini, V., Sburlea, A., & Müller-Putz, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-55413-x · Cited by 22

How many people can control a motor imagery based BCI using common spatial patterns?

Ortner, R., Scharinger, J., Lechner, A., & Guger, C. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146595 · Cited by 23

Controlling hand-assistive devices: utilizing electrooculography as a substitute for vision

Hao, Y., Controzzi, M., Cipriani, C., Popović, D. B., Yang, X. S., Chen, W., Zheng, X., & Carrozza, M. C. (2013) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2229949 · Cited by 23

An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm

Li, R., Zhang, X., Lu, Z., Liu, C., Li, H., Sheng, W., & Odekhe, R. O. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00943 · Cited by 23

Semi-autonomous navigation of an assistive robot using low throughput interfaces

Xavier Perrin, A. (2009) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-006014036 · Cited by 23

The Riemannian spatial pattern method: mapping and clustering movement imagery using Riemannian geometry

Larzabal, C., Auboiroux, V., Karakas, S., Charvet, G., Benabid, A., Chabardès, S., Costecalde, T., & Bonnet, S. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abf291 · Cited by 24

Writing through a robot: A proof of concept for a brain–machine interface

Pérez-Marcos, D., Buitrago, J. A., & Giraldo, F. D. (2011) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2011.06.005 · Cited by 22

Single-paradigm and hybrid brain computing interfaces and their use by disabled patients

de Neeling, M. G. J., & Hulle, M. M. V. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab2706 · Cited by 22

Progress and Prospects in EEG-Based Brain-Computer Interface: Clinical Applications in Neurorehabilitation

Machado (2013) · Journal of Rehabilitation Robotics · doi:10.12970/2308-8354.2013.01.01.4 · Cited by 24

Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control

Müller-Putz, G., Kobler, R. J., Pereira, J., Lopes-Dias, C., Hehenberger, L., Mondini, V., Martínez-Cagigal, V., Srisrisawang, N., Pulferer, H. S., Batistić, L., et al. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.841312 · Cited by 26

Optimizing Motor Imagery Parameters for Robotic Arm Control by Brain-Computer Interface

Hayta, Ü., Irimia, D.-C., Guger, C., Erkutlu, İ., & Guzelbey, I. H. (2022) · Brain Sciences · doi:10.3390/brainsci12070833 · Cited by 20

Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant

Pulferer, H. S., Ásgeirsdóttir, B., Mondini, V., Sburlea, A. I., & Müller-Putz, G. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac689f · Cited by 21

Using machine learning to reveal the population vector from EEG signals

Kobler, R. J., Almeida, I. A. T., Sburlea, A. I., & Muller-Putz, G. R. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7490 · Cited by 20

Information transfer rate in fMRI experiments measured using mutual information theory

Ward, B. D., & Mazaheri, Y. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.06.027 · Cited by 20

EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review

Cioffi, E., Hutber, A., Molloy, R., Murden, S., Yurkewich, A., Kirton, A., Lin, J., Gimeno, H., & McClelland, V. M. (2024) · Clinical Neurophysiology · doi:10.1016/j.clinph.2024.08.009 · Cited by 19

Intuitive Interaction with Robots – Technical Approaches and Challenges

Kirchner, E. A., Fernández, J. D. G., Kampmann, P., Schröer, M., Metzen, J. H., & Kirchner, F. (2015) · Springer eBooks · doi:10.1007/978-3-658-09994-7_8 · Cited by 19

An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials

Li, R., Zhang, X., Li, H., Zhang, L., Lu, Z., & Chen, J. (2018) · Brain Research · doi:10.1016/j.brainres.2018.05.018 · Cited by 19

A Time-Frequency Distribution-Based Approach for Decoding Visually Imagined Objects Using EEG Signals

Alazrai, R., Al-Saqqaf, A., Al‐Hawari, F., Alwanni, H., & Daoud, M. I. (2020) · IEEE Access · doi:10.1109/access.2020.3012918 · Cited by 19

Brain-Computer Interface Systems Used for Virtual Reality Control

Pfurtscheller, G., Leeb, R., Faller, J., & Neuper, C. (2011) · InTech eBooks · doi:10.5772/13467 · Cited by 19

Neurofeedback and Brain–Computer Interfaces

Wyckoff, S. N., & Birbaumer, N. (2014) · Wiley eBooks · doi:10.1002/9781118453940.ch15 · Cited by 20

Decoding covert speech for intuitive control of brain-computer interfaces based on single-trial EEG: a feasibility study

Tøttrup, L., Leerskov, K., Hadsund, J. T., Kamavuako, E. N., Kaseler, R. L., & Jochumsen, M. (2019) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2019.8779499 · Cited by 18

xBCI: A Generic Platform for Development of an Online BCI System

Susila, I. P., Kanoh, S., Miyamoto, K., & Yoshinobu, T. (2010) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.20560 · Cited by 18

Skin potential response in letter recognition task as an alternative communication channel for individuals with severe motor disability

Tsukahara, R., & Aoki, H. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00257-2 · Cited by 18

Effect of subject training on a movement-related cortical potential-based brain-computer interface

Jochumsen, M., Niazi, I., Nedergaard, R. W., Navid, M. S., & Dremstrup, K. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.11.012 · Cited by 19

HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury

Höller, Y., Thomschewski, A. C., Uhl, A., Bathke, A. C., Nardone, R., Leis, S., Trinka, E., & Höller, P. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00955 · Cited by 17

An adaptive brain actuated system for augmenting rehabilitation

Roset, S. A., Gant, K. L., Prasad, A., & Sanchez, J. C. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00415 · Cited by 17

Reconstruction of reaching movement trajectories using electrocorticographic signals in humans

Talakoub, O., Márquez-Chin, C., Popović, M. R., Navarro, J., Fonoff, E. T., Hamani, C., & Wong, W. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182542 · Cited by 17

A deep learning approach for decoding visually imagined digits and letters using time–frequency–spatial representation of EEG signals

Alazrai, R., Abuhijleh, M., Ali, M. Z., & Daoud, M. I. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117417 · Cited by 16

Brain-computer interface using near-infrared spectroscopy for rehabilitation

Yanagisawa, K., Asaka, K., Sawai, H., TSUNASHIMA, H., Nagaoka, T., Tsujii, T., & Sakatani, K. (2010) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2010.5669837 · Cited by 16

Brain Sensors and Signals

Schalk, G., & Mellinger, J. (2010) · Springer eBooks · doi:10.1007/978-1-84996-092-2_2 · Cited by 16

Post-Adaptation Effects in a Motor Imagery Brain-Computer Interface Online Coadaptive Paradigm

Cunha, J. D., Perdikis, S., Halder, S., & Scherer, R. (2021) · IEEE Access · doi:10.1109/access.2021.3064226 · Cited by 18

Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

Robinson, N., Chouhan, T. S., Mihelj, E., Kratka, P., Debraine, F., Wenderoth, N., Guan, C., & Lehner, R. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.648275 · Cited by 17

Restoration of Hand Function in Stroke and Spinal Cord Injury

Kamper, D. G. (2016) · Springer eBooks · doi:10.1007/978-3-319-28603-7_15 · Cited by 17

The Graz Brain-Computer Interface

Pfurtscheller, G., Brunner, C., Leeb, R., Scherer, R., Muller-Putz, G. R., & Neuper, C. (2009) · The frontiers collection · doi:10.1007/978-3-642-02091-9_5 · Cited by 16

Feasibility of a Hybrid Brain-Computer Interface for Advanced Functional Electrical Therapy

Savić, A. M., Malešević, N., & Popović, M. B. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/797128 · Cited by 17

Motor prediction in Brain-Computer Interfaces for controlling mobile robots

Geng, T., & Gan, J. Q. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649232 · Cited by 16

Brain–Computer Interface—FES Integration: Towards a Hands-free Neuroprosthesis Command System

Boord, P., Barriskill, A., Craig, A. R., & Nguyen, H. T. (2004) · Neuromodulation Technology at the Neural Interface · doi:10.1111/j.1094-7159.2004.04212.x · Cited by 16

Chapter 62 Brain-computer communication based on the dynamics of brain oscillations

Pfurtscheller, G., Graimann, B., Huggins, J. E., & Levine, S. P. (2004) · Supplements to Clinical neurophysiology · doi:10.1016/s1567-424x(09)70398-8 · Cited by 17

Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public

Grevet, E., Forge, K., Tadiello, S., Izac, M., Amadieu, F., Brunel, L. C., Pillette, L., Py, J., Gasq, D., & Jeunet, C. (2023) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2022.1082901 · Cited by 15

Movement prediction for a lower limb exoskeleton using a conditional restricted Boltzmann machine

Chong, E., & Park, F. C. (2016) · Robotica · doi:10.1017/s0263574716000795 · Cited by 15

Real-time, time–frequency mapping of event-related cortical activation

Cheung, C., & Chang, E. F. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/4/046018 · Cited by 15

An OpenViBE-based brainwave control system for Cerebot

Zhao, J., Meng, Q., Li, W., Li, M., Sun, F., & Chen, G. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739622 · Cited by 14

Systems Neuroscience and Rehabilitation

Kansaku, K., & Cohen, L. G. (2011) · Springer eBooks · doi:10.1007/978-4-431-54008-3 · Cited by 15

SSVEP-based hierarchical architecture for control of a humanoid robot with mind

Zhao, J., Meng, Q., Li, W., Li, M., & Chen, G. (2014) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2014.7053097 · Cited by 14

Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients

López‐Larraz, E., Bibián, C., Birbaumer, N., & Ramos‐Murguialday, A. (2017) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2017.8009363 · Cited by 14

Social Environments, Mixed Communication and Goal-Oriented Control Application Using a Brain-Computer Interface

Edlinger, G., & Guger, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21663-3_59 · Cited by 16

EEG-Based Brain–Computer Interfaces

Pfurtscheller, G., Brunner, C., & Neuper, C. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190228484.003.0047 · Cited by 14

Steuerungs- und Signalverarbeitungskonzepte für eine multifunktionale Handprothese (Control and Signal Processing Concepts for a Multifunctional Hand Prosthesis)

Reischl, M., Mikut, R., Pylatiuk, C., Schulz, S., Beck, S., & Bretthauer, G. (2002) · at - Automatisierungstechnik · doi:10.1524/auto.2002.50.6.279 · Cited by 14

‘Virtual Keyboard’ Controlled by Spontaneous EEG Activity

Obermaier, B., Müller, G., & Pfurtscheller, G. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44668-0_89 · Cited by 13

Brains and Blocks

Crawford, C. S., & Gilbert, J. E. (2019) · ACM Transactions on Computing Education · doi:10.1145/3335815 · Cited by 14

Decoding Ipsilateral Finger Movements from ECoG Signals in Humans

Liu, Y., Sharma, M., Gaona, C. M., Breshears, J. D., Roland, J. L., Freudenburg, Z. V., Leuthardt, E. C., & Weinberger, K. Q. (2010) · Cited by 13

BCI-Triggered functional electrical stimulation therapy for upper limb

Márquez-Chin, C., Marquis, A., & Popović, M. R. (2016) · European Journal of Translational Myology · doi:10.4081/ejtm.2016.6222 · Cited by 13

A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control

Kuo, C., Chen, J.-W., Yang, Y., Lan, Y.-H., Lu, S.-W., Wang, C.-F., Lo, Y., Lin, C.-L., Lin, S.-H., Chen, P.-C., et al. (2022) · Biosensors · doi:10.3390/bios12050312 · Cited by 12

Progress and prospects in neurorehabilitation: clinical applications of stem cells and brain–computer interface for spinal cord lesions

Gongora, M., Peressutti, C., Machado, S. E. D. C., Teixeira, S. S., Velasques, B., & Ribeiro, P. F. M. (2012) · Neurological Sciences · doi:10.1007/s10072-012-1232-5 · Cited by 12

Peering into the mind? The ethics of consumer neuromonitoring devices

McCall, I. C., & Wexler, A. (2020) · Developments in neuroethics and bioethics · doi:10.1016/bs.dnb.2020.03.001 · Cited by 12

The Sensitivity of Single-Trial Mu-Suppression Detection for Motor Imagery Performance as Compared to Motor Execution and Motor Observation Performance

Xu, K., Huang, Y.-Y., & Duann, J. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00302 · Cited by 14

Architectures of laboratory-PC and mobile pocket PC brain-computer interfaces

Edlinger, G., Krausz, G., Laundl, F., Niedermayer, I., & Guger, C. (2005) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/cne.2005.1419568 · Cited by 12

A Bibliometric Review of Brain–Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics

Chio, N., & Quiles, E. (2024) · Sensors · doi:10.3390/s25010154 · Cited by 12

Low temperature nanointegration for emerging biomedical applications

Howlader, M. M. R., & Doyle, T. E. (2011) · Microelectronics Reliability · doi:10.1016/j.microrel.2011.08.018 · Cited by 11

Temperament Predictors of Motor Imagery Control in BCI

Zapała, D., Małkiewicz, M., Francuz, P., Kołodziej, M., & Majkowski, A. (2019) · Journal of Psychophysiology · doi:10.1027/0269-8803/a000252 · Cited by 11

Novel approach for electromyography-controlled prostheses based on facial action

Zhang, X., Li, R., Li, H., Lu, Z., Hu, Y. S., & Alhassan, A. B. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02236-3 · Cited by 11

Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction

Hayashi, M., Tsuchimoto, S., Mizuguchi, N., Miyatake, M., Kasuga, S., & Ushiba, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab221a · Cited by 11

fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface

Kline, A. S., Forkert, N. D., Felfeliyan, B., Pittman, D. J., Goodyear, B. G., & Ronsky, J. L. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109339 · Cited by 11

Inferring Functional Brain States Using Temporal Evolution of Regularized Classifiers

Zhdanov, A. V., Hendler, T., Ungerleider, L. G., & Intrator, N. (2007) · Computational Intelligence and Neuroscience · doi:10.1155/2007/52609 · Cited by 11

Analysis and classification of EEG signals using probabilistic models for brain computer interfaces

Chiappa, S. (2006) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3547 · Cited by 11

Human-Computer Confluence for Rehabilitation Purposes after Stroke

Ortner, R., Ram, D., Kollreider, A., Pitsch, H., Wojtowicz, J. E., & Edlinger, G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39420-1_9 · Cited by 11

Brain Training with Neurofeedback

Perronnet, L., Lécuyer, A., Lotte, F., Clerc, M., & Barillot, C. (2016) · Wiley eBooks · doi:10.1002/9781119144977.ch13 · Cited by 11

A multimenu system based on the P300 component as a time saving procedure for communication with a brain-computer interface

Jarmolowska, J., Turconi, M. M., Busan, P., Mei, J., & Battaglini, P. P. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00039 · Cited by 10

Predictive-spectral-spatial preprocessing for a multiclass brain-computer interface

Coyle, D., Satti, A. R., & McGinnity, T. M. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596756 · Cited by 10

Bio-Interfaces

Zuanon, R. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-0942-6.ch001 · Cited by 10

Brain-computer interface adaptation for an end user to compete in the Cybathlon

Schwarz, A., Steyrl, D., & Muller-Putz, G. R. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844499 · Cited by 10

Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network

Hermosilla, D. M., Codorniú, R. T., Lamar-Carbonell, S., Zamora, R. S., Tamayo-Pacheco, J. J., Mayor, J. J. V., & Delisle-Rodríguez, D. (2023) · Sensors · doi:10.3390/s23020703 · Cited by 13

LFP beta power predicts cursor stationarity in BMI task

Ranade, G. V., Ganguly, K., & Carmena, J. M. (2009) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2009.5109338 · Cited by 10

Effect of Short Term Meditation on Brain-Computer Interface Performance

Tan, Y.-Q., Tan, L.-F., Mok, S.-Y., & Goh, S.-Y. (2015) · Journal of Medical and Bioengineering · doi:10.12720/jomb.4.2.135-138 · Cited by 11

Laboratory PC and Mobile Pocket PC Brain-Computer Interface Architectures

Edlinger, G., & Guger, C. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1615689 · Cited by 10

Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study

Almanna, M. A., Elkaim, L. M., Alvi, M. A., Levett, J. J., Li, B., Mamdani, M., Al-Omran, M. A., & Alotaibi, N. M. (2025) · JMIR Formative Research · doi:10.2196/60859 · Cited by 10

Preserved Foot Motor Cortex in Patients With Complete Spinal Cord Injury

Koenraadt, K. L. M., Duysens, J., Rijken, H., van Nes, I. J. W., & Keijsers, N. (2013) · Neurorehabilitation and neural repair · doi:10.1177/1545968313508469 · Cited by 9

Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction

Jia, T., Liu, K., Qian, C., Li, C., & Ji, L. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108909 · Cited by 9

Cold Laser Micro-Machining of PDMS as an Encapsulation Layer for Soft Implantable Neural Interface

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