The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment
Abstract
Objective This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication.Methods EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS).Results The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min.Conclusions The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.
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“Direct evidence of learned SMR modulation at the BCI feature level is, in fact, rare or incomplete, derived in able-bodied populations and not longitudinal [10,23,26,28,38–41].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [10]
In the citing paper
“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 [10]
In the citing paper
“Furthermore, [10] and [42] substantiate learning effects with event-related desynchronization/synchronization (ERD/ERS) maps and SMR topographies, respectively, over 3–4 time points throughout the training period.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [10]
In the citing paper
“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 [10]
Citations
Cites
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
Self-citationBootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & HINKLEY, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,478
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
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
Self-citationA 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
Self-citationToward Direct Brain-Computer Communication
Vidal, J. J. (1973) · Annual Review of Biophysics and Bioengineering · doi:10.1146/annurev.bb.02.060173.001105 · Cited by 1,311
Plasticity and Primary Motor Cortex
Sanes, J. N., & Donoghue, J. P. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.393 · Cited by 1,202
An EEG-based brain-computer interface for cursor control
Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90040-b · Cited by 1,117
Crone, N. E. (1998) · Brain · doi:10.1093/brain/121.12.2271 · Cited by 943
Porro, C. A., Francescato, M. P., Cettolo, V., Diamond, M. E., Baraldi, P., Zuiani, C., Bazzocchi, M., & di Prampero, P. E. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-23-07688.1996 · Cited by 924
Cited by
Sorted by citations
Items 201–300 of 317
Motor Imagery Under Distraction— An Open Access BCI Dataset
Brandl, S., & Blankertz, B. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.566147 · Cited by 11
An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets
Khare, S. K., Gaikwad, N. B., & Bokde, N. D. (2022) · Sensors · doi:10.3390/s22218128 · Cited by 10
Prototype of an auto-calibrating, context-aware, hybrid brain-computer interface
Faller, J., Torrellas, S., Miralles, F., Holzner, C., Kapeller, C., Guger, C., Bund, J., Müller-Putz, G., & Scherer, R. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346306 · Cited by 10
MINDDESKTOP - Computer Accessibility for Severely Handicapped
Ossmy, O., Tam, O., Puzis, R., Rokach, L., Inbar, O., & Elovici, Y. (2011) · International Conference on Enterprise Information Systems · doi:10.5220/0003500803160320 · Cited by 10
Zhang, H., Liang, J., Liu, Y., Wang, H., & Zhang, L. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-37835-5_32 · Cited by 10
Pokorny, C., Pichler, G., Erlbeck, H., Lesenfants, D., Habbal, D., Noirhomme, Q., Risetti, M., & Mattia, D. (2013) · Cited by 9
Place of electroencephalograpic biofeedback for attention-deficit/hyperactivity disorder
Hirshberg, L. (2007) · Expert Review of Neurotherapeutics · doi:10.1586/14737175.7.4.315 · Cited by 9
Control signal for a mechatronic hand orthosis aimed for neurorehabilitation
Cantillo-Negrete, J., Carino-Escobar, R. I., Elı́as-Viñas, D., & Gutiérrez-Martínez, J. (2015) · Pan American Health Care Exchanges · doi:10.1109/pahce.2015.7173328 · Cited by 9
Use Your Brain (and Light) for Innovative Human-Machine Interfaces
Biondi, F., & Skrypchuk, L. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-41956-5_10 · Cited by 9
Construction of neural nets in brain-computer interface for robot arm steering
Plechawska–Wójcik, M., Wolszczak, P., Cechowicz, R., & Łygas, K. (2016) · Conference on Human System Interactions · doi:10.1109/hsi.2016.7529656 · Cited by 9
EEG ‐Based Brain‐Computer Interface System
Pfurtscheller, G., Graimann, B., & Neuper, C. (2006) · Wiley eBooks · doi:10.1002/9780471740360.ebs1309 · Cited by 10
Self-citationA review on brain imaging techniques for BCI applications
Bhattacharyya, S., Khasnobish, A., Ghosh, P., Mazumder, A., & Tibarewala, D. N. (2017) · Medical Imaging · doi:10.4018/978-1-5225-0571-6.ch012 · Cited by 8
Signal Processing for Non-Invasive Brain Biomarkers of Sensorimotor Performance and Brain Monitoring
Rodolphe, J., Oh, H., Trent, J., Bradley, D., & Jose, L. (2010) · InTech eBooks · doi:10.5772/8536 · Cited by 7
A PREDICTIVE SPELLER FOR A BRAIN-COMPUTER INTERFACE BASED ON MOTOR IMAGERY
Sbattella, L., & Tedesco, R. (2008) · Cited by 8
Teo, W., White, D., & Macpherson, H. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812892-3.00003-0 · Cited by 6
Edlinger, G., Rizzo, C., & Guger, C. (2011) · Springer eBooks · doi:10.1007/978-3-540-74658-4_52 · Cited by 7
Neshige, R., Murayama, N., Igasaki, T., Tanoue, K., Kurokawa, H., & Asayama, S. (2007) · Brain Research · doi:10.1016/j.brainres.2006.12.003 · Cited by 7
Enabling bio-feedback using real-time fMRI
Ohlsson, H., Rydell, J., Brun, A., Roll, J., Andersson, M., Ynnerman, A., & Knutsson, H. E. (2008) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2008.4738759 · Cited by 7
Faller, J., Solis‐Escalante, T., Costa, U., Opisso, E., Medina, J., Scherer, R., & Muller-Putz, G. R. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6696099 · Cited by 6
Wang, H., Song, A., Li, B., Xu, B., & Li, Y. (2015) · Technology and Health Care · doi:10.3233/thc-150960 · Cited by 6
Gutiérrez-Martínez, J., Cantillo-Negrete, J., Carino-Escobar, R. I., & Elı́as-Viñas, D. (2013) · Investigación en Discapacidad · Cited by 6
A neurofeedback training paradigm for motor imagery based Brain-Computer Interface
Xia, B., Zhang, Q., Xie, H., & Li, J. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252576 · Cited by 6
Feature recognition of multi-class imaginary movements in brain-computer interface
Wan, B., Liu, Y., Ming, D., Qi, H., Wang, Y., & Zhang, R. (2009) · IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS) · doi:10.1109/vecims.2009.5068903 · Cited by 6
Brain-Computer Interfacing for Users with Cerebral Palsy, Challenges and Opportunities
Daly, I. P., Billinger, M., Scherer, R., & Muller-Putz, G. R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39188-0_67 · Cited by 6
Goljahani, A., D’Avanzo, C., Silvoni, S., Tonin, P., Piccione, F., & Sparacino, G. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/731046 · Cited by 6
Scherer, R., & Rao, R. P. N. (2011) · IGI Global eBooks · doi:10.4018/978-1-60566-206-0.ch015 · Cited by 7
EEG Based BCI—Control Applications
Bansal, D., & Mahajan, R. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814687-3.00006-5 · Cited by 5
Immobilis in mobili: performing arts, BCI, and locked-in syndrome
Aparicio, A. (2015) · Brain-Computer Interfaces · doi:10.1080/2326263x.2015.1100366 · Cited by 5
Daly, J., Armstrong, E., Pinegger, A., Muller-Putz, G. R., & Martin, S. (2015) · International Journal on Recent and Innovation Trends in Computing and Communication · Cited by 5
Towards Moving Virtual Arms Using Brain-Computer Interface
Riascos, J. A., Villa, S., Maciel, A., Nedel, L., & Barone, D. A. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22514-8_43 · Cited by 5
A Self-Paced Two-State Mental Task-Based Brain-Computer Interface with Few EEG Channels
Faradji, F., Ward, R. K., & Birch, G. E. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.83425 · Cited by 5
Perego, P., Turconi, A. C., Andreoni, G., & Gagliardi, C. (2014) · Brain-Computer Interfaces · doi:10.1080/2326263x.2014.979727 · Cited by 5
Serrano, J. I., del Castillo, M. D., Bayón, C., Ramírez, O., Lara, S. L., Martínez-Caballero, I., & Rocón, E. (2017) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-57132-4_8 · Cited by 5
Machado, S. E. D. C., da Cunha, M. M., Velasques, B., Minc, D., Bastos, V. H., Budde, H., Cagy, M., Piedade, R. A. M., & Ribeiro, P. F. M. (2019) · Revista Neurociências · doi:10.34024/rnc.2009.v17.8525 · Cited by 5
A brain-computer interface test-bench based on EEG signals for research and student training
Raif, P., Mahmud, M., Hussain, A., Kłos-Witkowska, A., & Suchanek, R. (2013) · IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) · doi:10.1109/cicare.2013.6583067 · Cited by 4
Lin, C., Hsieh, T.-Y., Liu, Y., Wu, S.-L., & Lin, Y.-Y. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.559 · Cited by 4
Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors
Gargava, P., & Asawa, K. (2016) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2017.457 · Cited by 4
A Review of fMRI as a Tool for Enhancing Eeg-Based Brain-Machine Interfaces
Barrios, L. J., del Castillo, M. D., Serrano, J. I., & Pons, J. L. (2012) · Applied Bionics and Biomechanics · doi:10.1155/2012/545182 · Cited by 4
Burmester, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-36630-0_5 · Cited by 4
Chatelle, C., Lesenfants, D., & Noirhomme, Q. (2017) · Springer eBooks · doi:10.1007/978-3-319-55964-3_4 · Cited by 3
Mind interactive multimedia system for disabled people
Jabeen, F., Tao, L., & Wang, X. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8302322 · Cited by 3
Electroencephalogram and Electrocardiogram in Human-Computer Interaction
Li, P., Qian, Y., & Si, N. (2022) · IEEE International Conference on Data Science and Computer Application (ICDSCA) · doi:10.1109/icdsca56264.2022.9988056 · Cited by 3
Brain computer interfaces for communication, mobility and motor recovery
Tabernig, C. B., Carrere, L. C., Atum, Y. V., & Acevedo, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-819877-3.00006-8 · Cited by 3
Alternative CSP approaches for multimodal distributed BCI data
Brandl, S., Müller, K., & Samek, W. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844816 · Cited by 3
The Future of Neural Interface Technology
Park, M. C., Goldman, M. A., Belknap, T. W., & Friehs, G. M. (2009) · Springer eBooks · doi:10.1007/978-3-540-69960-6_193 · Cited by 3
A Co-adaptive Training Paradigm for Motor Imagery Based Brain-Computer Interface
Xia, B., Zhang, Q., Xie, H., Li, S., Li, J., & He, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31346-2_49 · Cited by 3
Adaptive hybrid brain-computer interaction: Ask a trainer for assistance!
Muller-Putz, G. R., Steyrl, D., & Faller, J. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943884 · Cited by 3
Extraction of functional information from ongoing brain electrical activity
Besserve, M., & Martinerie, J. M. (2011) · IRBM · doi:10.1016/j.irbm.2011.01.001 · Cited by 3
Ware, M. P., Lightbody, G., McCullagh, P. J., Mulvenna, M. D., Martin, S., & Thomson, E. (2010) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2010.5687690 · Cited by 3
Scherer, R., Muller-Putz, G. R., Friedrich, E. V. C., Pammer‐Schindler, V., Wilding, K., Keller, S., & Pirker, J. (2015) · Springer eBooks · doi:10.1007/978-981-4560-52-8_6-1 · Cited by 3
Appling of Neural Networks to Classification of Brain-Computer Interface Data
Plechawska–Wójcik, M., & Wolszczak, P. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-34099-9_37 · Cited by 3
Scherer, R., Muller-Putz, G. R., Friedrich, E. V. C., Pammer‐Schindler, V., Wilding, K., Keller, S., & Pirker, J. (2016) · Springer eBooks · doi:10.1007/978-981-4560-50-4_6 · Cited by 3
Cossio, E. G., Fernández, C., Gaviria, M. E., Palacio, C., Alvarán, L., & Villa, R. A. T. (2012) · Revista Facultad de Ingeniería Universidad de Antioquia · doi:10.17533/udea.redin.13651 · Cited by 2
Decoding of movement characteristics for Brain Computer Interfaces application
Gu, Y. (2009) · Cited by 1
Ivanov, N., & Chau, T. T. (2024) · Brain-Apparatus Communication A Journal of Bacomics · doi:10.1080/27706710.2024.2366193 · Cited by 2
Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
Eklund, A. (2010) · Cited by 2
Lightbody, G., Allison, B. Z., & McCullagh, P. J. (2013) · Communications in medical and care compunetics · doi:10.1007/978-3-642-38643-5_23 · Cited by 2
BCI-controlled mechatronic devices, systems, and environments
Mikołajewski, D., Tomaszewska, E., & Kaczmarek, M. (2014) · Repository of Kazimierz Wielki University · Cited by 2
Design and Implementation of an Improved BCI System Based on Alpha Rhythm
Wei, B., Wu, X., & Zhang, D. (2009) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2009.5162588 · Cited by 2
A brain-computer interface algorithm based on Hidden Markov models and dimensionality reduction
Argunşah, A. Ö., & Çetin, M. (2010) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2010.5654406 · Cited by 2
IEETA brain computer interface technologies
Georgieva, P., da Silva, F. M. T. P., & Figueiredo, N. (2013) · International Journal of Computational Intelligence Studies · doi:10.1504/ijcistudies.2013.057645 · Cited by 2
Cossio, E. G., Fernández, C., Gaviria, M. E., Palacio, C., Alvarán, L., & Villa, R. A. T. (2011) · Radboud Repository (Radboud University) · Cited by 2
Towards Communication in the Completely Locked-In State: Neuroelectric Semantic Conditioning BCI
Massari, D. D., Ruf, C. A., Furdea, A., Halder, S., Matuz, T., & Birbaumer, N. (2013) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-642-36083-1_11 · Cited by 2
Self-citationDevelopment of the Heuristic BCI System with Learning-Type Fuzzy Template Matching Method
Oda, T., & Kudoh, S. N. (2020) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.32.3_737 · Cited by 2
Electroencephalogram based Brain Computer Interface System Analysis
(2020) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.c1092.0193s20 · Cited by 1
Tseng, R.-Y., & Yi–Luen, E. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2653-9.ch006 · Cited by 2
Human-Computer Interaction System for Communications and Control
Radeva, S., & Radev, D. I. (2015) · doi:10.1109/cit/iucc/dasc/picom.2015.300 · Cited by 2
MindDesktop: a general purpose brain computer interfacePreprint
Ossmy, O., Tam, O., Puzis, R., Rokach, L., Inbar, O., & Elovici, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07490 · Cited by 2
Brain-Computer Interface: A Communication Aid?
Chatelle, C., Lugo, Z., Noirhomme, Q., Sorger, B., & Lulé, D. E. (2012) · Springer eBooks · doi:10.1007/978-1-4471-2440-5_7 · Cited by 1
A method for assessing the usability of an on screen display for a brain-computer interface
Ware, M. P., Lightbody, G., McCullagh, P. J., Mulvenna, M. D., Martin, S., & Thomson, E. (2014) · International Journal of Computers in Healthcare · doi:10.1504/ijcih.2014.065811 · Cited by 1
Remote Control System of an Electric Car Based on the Alpha Wave in EEG
Liu, Q., Zhao, X., Wan, B., & Zhao, L. (2006) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2006.1713824 · Cited by 1
A camera based human computer interaction through virtual keyboard assistant
Uma, M., Sheela, T., & Vivek, V. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/402/1/012182 · Cited by 1
Development of a Silent Speech Interface for Augmented Reality Applications
Walck, C. D., Rivas, T., Flanagan, R., & Fornito, M. J. (2022) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-031-10015-4_18 · Cited by 1
Brain Machine Interface — IEETA case study
Georgieva, P., da Silva, F. M. T. P., & Figueiredo, N. (2012) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2012.6335164 · Cited by 1
ARTIFACTS CLASSIFICATION IN EEG SIGNALS BASED ON TEMPORAL AVERAGE STATISTICS
Qayoom, A., Wahab, A., Kamaruddin, N., & Zahid, Z. (2015) · Jurnal Teknologi · doi:10.11113/jt.v77.6251 · Cited by 1
EEG Signal Pr 46. EEG Signal Processing for Brain-Computer Interfaces
Georgieva, P., da Silva, F. M. T. P., Milanova, M. G., & Kasabov, N. K. (2014) · Cited by 1
Jain, D. C., Rajawat, A. S., Chouhan, H., & Pawar, V. P. (2011) · Nirma University International Conference on Engineering · doi:10.1109/nuicone.2011.6153234 · Cited by 1
BCI for Lighting Control of Smart Home Based on Alpha-Block
Zhang, G., Zhang, S., & Jin, J. (2019) · International Conference on Robotics, Control and Automation Engineering (RCAE) · doi:10.1145/3372047.3372108 · Cited by 1
Topographic Mapping, Frequency Analysis, and Other Digital Techniques in Electroencephalography
Nuwer, M. R. (2005) · Elsevier eBooks · doi:10.1016/b978-0-443-06647-4.50013-6 · Cited by 1
What if I had a third arm? An EEG study of a supernumerary BCI systemPreprint
Riascos, J. A., Villa, S., Maciel, A., Nedel, L., & Barone, D. A. C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/817205 · Cited by 1
Near-infrared Spectroscopy Signal Classification: Towards a Brain-computer Interface
Tai, K. (2008) · TSpace (University of Toronto) · Cited by 1
Caracterización de la sincronía de fase de EEG para su aplicación en Interfaces Cerebro-Computadora
García, M. Á. P. (2020) · doi:10.24275/uami.vq27zn72g · Cited by 1
Effectiveness of neurofeedback interventions in cerebral palsy: A systematic reviewPreprint
Abay, S. N., Alaerts, K., Dan, B., Monbaliu, E., & Bekteshi, S. (2025) · medRxiv · doi:10.1101/2025.08.14.25333687 · Cited by 1
The Thought Translation Device: communication by means of EEG self-regulation for locked-in patients
Kübler, A., & Birbaumer, N. (2004) · Elsevier eBooks · doi:10.1016/b978-0-443-07247-5.50011-0 · Cited by 1
Self-citationLa técnica de electroencefalografía en la evaluación de la actividad cortical motora.
Lage, E. F. (2009) · Gredos (University of Salamanca)
An HMM-PCA approach for EEG-based brain computer interfaces (BCIs)
Argunşah, A. Ö. (2010) · Sabanci University
Mühl, C., Heylen, D., Nijholt, A., Cohn, J., Nijholt, A., & Pantić, M. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349457
Research on the application of the brain-computer interface based on electrocorticographic signals
Shaobai, Z., & Chen, Y. (2013) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2013.6561164
Covert orienting of visuospatial attention in a brain-computer interface for communication
Marchetti, M. (2012) · Padua Research Archive (University of Padova)
Interface cerveau-ordinateur : Une aide à la communication ?
Chatelle, C., Lugo, Z., Noirhomme, Q., Sorger, B., & Lulé, D. E. (2011) · Springer eBooks · doi:10.1007/978-2-8178-0127-8_8
Rehabilitation with Brain-Computer Systems
Muller-Putz, G. R. (2008)
Efficiency analysis of voluntary control of human’s EEG spectral characteristics
Kiroy, V. N., Aslanyan, E. V., Lazurenko, D. M., Minyaeva, N. R., & Бахтин, О. М. (2016) · Journal of Integrative Neuroscience · doi:10.1142/s0219635216500072
Chai, R. (2014) · UTS ePRESS (University of Technology Sydney)
Electromagnetic signals in noninvasive brain-computer interfaces
Kauhanen, L. (2008) · Aaltodoc (Aalto University)
Interfejsy BCI jako nowy kanał komunikacji dla osób z niepełnosprawnością ruchową
Sójka, A., Janiak, K., Sikorska-Nowak, A., & Smółka, K. (2016) · Roczniki Kolegium Analiz Ekonomicznych
Blain, S. (2010) · TSpace (University of Toronto)
Detecting Emotional Response to Music using Near-infrared Spectroscopy of the Prefrontal Cortex
Moghimi, S. (2014) · TSpace (University of Toronto)
Metadata
- Authors
- Neuper, C., Müller, G. R., Kübler, A., Birbaumer, N., & Pfurtscheller, G.
- Affiliations
- Graz University of Technology (AT)
- University of Tübingen (DE)
- University of Trento (IT)
- Corresponding author
- Neuper, C
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2003-03
- Volume
- 114
- Issue
- 3
- Pages
- 399-409
- ISSN
- 1388-2457 (ISSN-L)
- OpenAlex
- W2018281395
- Cited by
- 317
- References
- 47
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 5 · 2020: 12 · 2019: 13 · 2018: 15 · 2017: 10 · 2016: 21 · 2015: 22 · 2014: 19 · 2013: 27 · 2012: 28
- FWCI
- 4.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- EEG-based brain-computer interface, severe cerebral palsy, locked-in syndrome, motor impairment, remote patient monitoring, event-related desynchronization, event-related synchronization, beta band, movement imagery, spelling, verbal communication
- MeSH terms
- Adult, Biofeedback, Psychology, Cerebral Palsy, Communication Devices for People with Disabilities, Communication Barriers, Electroencephalography, Humans, Male, Motor Cortex, Paralysis, Severity of Illness Index, Somatosensory Cortex, User-Computer Interface
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.
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