Cited by 102EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyMuscle activation and electromyography studies

The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.

Neurofeedback Training for BCI Control

Neuper, Christa, Pfurtscheller, Gert·2009·The Frontiers Collection

DOI 10.1007/978-3-642-02091-9_4

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/978-3-642-02091-9_4

Cites

69 of 69 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
Self-citation
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-citation
Brain–machine interfaces: past, present and future
Lebedev, M. A., & Nicolelis, M. A. L. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.07.004 · Cited by 1,875
The functional states of the thalamus and the associated neuronal interplay.
Steriade, M., & Llinas, R. R. (1988) · Physiological Reviews · doi:10.1152/physrev.1988.68.3.649 · Cited by 1,746
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
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
Neural mechanisms underlying brain waves: from neural membranes to networks
Lopes da Silva, F. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90044-5 · Cited by 1,112
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
Self-citation
Activation of human primary motor cortex during action observation: A neuromagnetic study
Hari, R., Forss, N., Avikainen, S., Kirveskari, E., Salenius, S., & Rizzolatti, G. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.15061 · Cited by 983
The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects
Blankertz, B., Dornhege, G., Krauledat, M., Müller, K., & Curio, G. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.01.051 · Cited by 941

Show all 68 references

Research works printed but not linked: 3

S. Othmer, S.F. Othmer, and D.A. Kaiser, EEG biofeedback: An emerging model for ist global efficacy. In J. Evans and A. Abarbanel (Eds.), Quantitative EEG and neurofeedback, Academic, pp. 243–310, (1999).
J.V. Hardt, The ups and downs of learning alpha feedback. Biofeedback Res Soc, 6, 118, (1975).
R. Ron-Angevin, A. Diaz-Estrella, and A. Reyes-Lecuona, Development of a brain-computer interface based on virtual reality to improve training techniques. Cyberpsychol Behav, 8, 353–354, (2005).

Sorted by citations

A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Could Anyone Use a BCI?
Allison, B. Z., & Neuper, C. (2010) · Human-computer interaction series · doi:10.1007/978-1-84996-272-8_3 · Cited by 379
Self-citation
Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design
Lotte, F., Larrue, F., & Mühl, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00568 · Cited by 307
Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features
Nguyen, C. H., Karavas, G. K., & Artemiadis, P. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa8235 · Cited by 289
Two Brains, One Game: Design and Evaluation of a Multiuser BCI Video Game Based on Motor Imagery
Bonnet, L., Lotte, F., & Lécuyer, A. (2013) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2012.2237173 · Cited by 187
Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns
Jeunet, C., N’Kaoua, B., Subramanian, S., Hachet, M., & Lotte, F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143962 · Cited by 182
Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review
Papanastasiou, G., Drigas, A. S., Skianis, C. A., & Lytras, M. D. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04250 · Cited by 179
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
Advances in user-training for mental-imagery-based BCI control
Jeunet, C., N’Kaoua, B., & Lotte, F. (2016) · Progress in brain research · doi:10.1016/bs.pbr.2016.04.002 · Cited by 161
Review of real brain-controlled wheelchairs
Fernández-Rodríguez, Á., Velasco-Álvarez, F., & Ron-Angevín, R. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/6/061001 · Cited by 102
Brain enhancement through cognitive training: a new insight from brain connectome
Taya, F., Sun, Y., Babiloni, F., Thakor, N. V., & Bezerianos, A. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00044 · Cited by 116
Brain-Machine Interfaces: A Tale of Two Learners
Perdikis, S., & Millán, J. D. R. (2020) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2019.2958200 · Cited by 92
Performance of Motor Imagery Brain-Computer Interface Based on Anodal Transcranial Direct Current Stimulation Modulation
Wei, P., He, W., Zhou, Y., & Wang, L. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2249111 · Cited by 83
A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey
Zhu, H. Y., Hieu, N. Q., Hoang, D. T., Nguyen, D. N., & Lin, C. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3387124 · Cited by 86
Asynchronous BCI Based on Motor Imagery With Automated Calibration and Neurofeedback Training
Kuś, R., Valbuena, D., Żygierewicz, J., Malechka, T., Graeser, A., & Durka, P. J. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2214789 · Cited by 73
Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review
Jeunet, C., Lotte, F., Batail, J.-M. V., Philip, P., & Micoulaud‐Franchi, J. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.03.013 · Cited by 67
Towards improved BCI based on human learning principles
Lotte, F., & Jeunet, C. (2015) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2015.7073024 · Cited by 67
The effect of multimodal and enriched feedback on SMR-BCI performance
Sollfrank, T., Ramsay, A., Perdikis, S., Williamson, J. H., Murray‐Smith, R., Leeb, R., Millán, J. D. R., & Kübler, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.06.004 · Cited by 63
Cooperation in mind: Motor imagery of joint and single actions is represented in different brain areas
Wriessnegger, S. C., Steyrl, D., Koschutnig, K., & Muller-Putz, G. R. (2016) · Brain and Cognition · doi:10.1016/j.bandc.2016.08.008 · Cited by 61
Continuous Tactile Feedback for Motor-Imagery Based Brain-Computer Interaction in a Multitasking Context
Jeunet, C., Vi, C. T., Spelmezan, D., N’Kaoua, B., Lotte, F., & Subramanian, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22701-6_36 · Cited by 58
Virtual reality for assessment of patients suffering chronic pain: a case study
Llobera, J., González-Franco, M., Pérez-Marcos, D., Valls‐Solé, J., Slater, M., & Sanchez-Vives, M. V. (2012) · Experimental Brain Research · doi:10.1007/s00221-012-3352-9 · Cited by 49
The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI
Zapała, D., Francuz, P., Zapała, E., Kopiś-Posiej, N., Wierzgała, P., Augustynowicz, P., Majkowski, A., & Kołodziej, M. (2017) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-017-9383-z · Cited by 46
Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers
Santos, E., San-Martin, R., & Fraga, F. J. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02769-3 · Cited by 42
Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing
Flanagan, K., & Saikia, M. J. (2023) · Sensors · doi:10.3390/s23208482 · Cited by 47
Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016
Novak, D., Sigrist, R., Gerig, N. J., Wyss, D., Bauer, R., Götz, U., & Riener, R. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00756 · Cited by 41
Enhancement of attention and cognitive skills using EEG based neurofeedback game
Thomas, K. P., Vinod, A. P., & Guan, C. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6695861 · Cited by 41
Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
Park, S., Ha, J., Kim, D., & Kim, L. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.732545 · Cited by 40
Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network
Lei, B., Liu, X., Liang, S., Hang, W., Wang, Q., Choi, K., & Qin, J. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2895064 · Cited by 37
Online detection of auditory attention with mobile EEG: closing the loop with neurofeedbackPreprint
Zink, R., Proesmans, S., Bertrand, A., Huffel, S. V., & Vos, M. D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/218727 · Cited by 37
Android Feedback-Based Training Modulates Sensorimotor Rhythms During Motor Imagery
Peñaloza, C., Alimardani, M., & Nishio, S. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2792481 · Cited by 36
Placebo hampers ability to self-regulate brain activity: A double-blind sham-controlled neurofeedback study
Kober, S. E., Witte, M., Grinschgl, S., Neuper, C., & Wood, G. M. D. O. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.025 · Cited by 38
Self-citation
Sensory Stimulation Training for BCI System Based on Somatosensory Attentional Orientation
Yao, L., Sheng, X., Mrachacz‐Kersting, N., Zhu, X., Farina, D., & Jiang, N. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2852755 · Cited by 34
Two player EEG-based neurofeedback ball game for attention enhancement
Shenjie, S., Thomas, K. P., Smitha, K. G., & Vinod, A. P. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974412 · Cited by 33
The TRAVEE System for a Multimodal Neuromotor Rehabilitation
Moldoveanu, A. D. B., Ferche, O.-M., Moldoveanu, F., Lupu, R. G., Cinteză, D., Irimia, D.-C., & Toader, C. (2019) · IEEE Access · doi:10.1109/access.2018.2886271 · Cited by 32
Virtual Reality Cognitive Gaming Based on Brain Computer Interfacing: A Narrative Review
Hadjiaros, M., Neokleous, K., Shimi, A. G., Avraamides, M. N., & Pattichis, C. S. (2023) · IEEE Access · doi:10.1109/access.2023.3247133 · Cited by 33
Neurofeedback learning modifies the incidence rate of alpha spindles, but not their duration and amplitude
Ossadtchi, A. E., Shamaeva, T., Okorokova, E. V., Moiseeva, V., & Lebedev, M. (2017) · Scientific Reports · doi:10.1038/s41598-017-04012-0 · Cited by 31
Tactile Sensation Assisted Motor Imagery Training for Enhanced BCI Performance: A Randomized Controlled Study
Zhong, Y., Yao, L., Wang, J., & Wang, Y. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3201241 · Cited by 30
Improving Performance of Motor Imagery-Based Brain–Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory Activity
Park, S., Ha, J., & Kim, L. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3237583 · Cited by 28
A physical learning companion for Mental-Imagery BCI User Training
Pillette, L., Jeunet, C., Mansencal, B., Nkambou, R., N’Kaoua, B., & Lotte, F. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.102380 · Cited by 31
Improvement of Neurofeedback Therapy for Improved Attention Through Facilitation of Brain Activity Using Local Sinusoidal Extremely Low Frequency Magnetic Field Exposure
Mehran, Y. Z., Firoozabadi, M., & Rostami, R. (2014) · Clinical EEG and Neuroscience · doi:10.1177/1550059414524403 · Cited by 25
An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
Duan, X., Xie, S., Xie, X., Obermayer, K., Cui, Y., & Wang, Z. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.625983 · Cited by 24
Neural correlates of user learning during long-term BCI training for the Cybathlon competition
Tortora, S., Beraldo, G., Bettella, F., Formaggio, E., Rubega, M., Felice, A. D., Masiero, S., Carli, R., Petrone, N., Menegatti, E., et al. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-01047-x · Cited by 24
Brain-Computer Interface: Challenges and Research Perspectives
Lupu, R. G., Ungureanu, F., & Cîmpanu, C. (2019) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs.2019.00071 · Cited by 22
Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review
Tsai, P.-C., Akpan, A., Tang, K., & Lakany, H. (2025) · BMC Geriatrics · doi:10.1186/s12877-025-05676-4 · Cited by 24
High Aptitude Motor-Imagery BCI Users Have Better Visuospatial Memory
Leeuwis, N., & Alimardani, M. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283259 · Cited by 17
Context-Aware Based Quality of Life Telemonitoring
Vargiu, E., Fernández, J. M., & Miralles, F. (2013) · Studies in computational intelligence · doi:10.1007/978-3-642-40621-8_1 · Cited by 16
Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings
Chang, Y., He, C., Tsai, B.-Y., & Ko, L. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.785562 · Cited by 15
Feedback Strategies for BCI Based Stroke Rehabilitation: Evaluation of Different Approaches
Lechner, A., Ortner, R., & Guger, C. (2014) · Biosystems & biorobotics · doi:10.1007/978-3-319-08072-7_75 · Cited by 14
Brain-Computer Interfaces for stroke rehabilitation: Evaluation of feedback and classification strategies in healthy users
Ortner, R., Irimia, D.-C., Scharinger, J., & Guger, C. (2012) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2012.6290800 · Cited by 14
Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback
Mahrooz, M. H., Fattahzadeh, F., & Gharibzadeh, S. (2023) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-023-09601-6 · Cited by 12
Biomechatronic Applications of Brain-Computer Interfaces
Novak, D. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812539-7.00008-8 · Cited by 13
A Brain-Computer Interface and Augmented Reality Neurofeedback to Treat ADHD: A Virtual Telekinesis Approach
Reddy, G. S. R., & Lingaraju, G. M. (2020) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct51615.2020.00045 · Cited by 12
Towards a spatial ability training to improve Mental Imagery based Brain-Computer Interface (MI-BCI) performance: A Pilot study
Teillet, S., Lotte, F., N’Kaoua, B., & Jeunet, C. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844803 · Cited by 11
New perspectives for cognitive rehabilitation: Could brain-computer interface systems benefit people with dementia?
da Silva‐Sauer, L., de la Torre‐Luque, A., Silva, J. S. C., & Fernández‐Calvo, B. (2018) · Psychology & Neuroscience · doi:10.1037/pne0000154 · Cited by 10
The Brain Computer Interface: Barriers to Becoming Pervasive
Lightbody, G., Galway, L., & McCullagh, P. J. (2014) · Human-computer interaction series · doi:10.1007/978-1-4471-6413-5_5 · Cited by 9
A foot motor imagery brain-computer interface with realistic visual feedback: preliminary evaluation in healthy and stroke subjects
Carrere, L. C., Escher, L., Gentiletti, G. G., & Tabernig, C. B. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-021-00164-1 · Cited by 9
Non–human primate epidural ECoG analysis using explainable deep learning technology
Choi, H., Lim, S., Min, K., Ahn, K.-H., Lee, K., & Jang, D. P. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac3314 · Cited by 8
Limbs Movement and Motor Imagery: an EEG Study
Grilo, M., Santos, E., Moraes, C. P. A., Mondejar, A., Ascencao, P. V., de Araújo, L. V., Neves, A. O., & Fraga, F. J. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8969891 · Cited by 7
Multichannel EEG sonification with ambisonics spatial sound environment
Rutkowski, T. M. (2014) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2014.7041817 · Cited by 7
A transfer learning-based feedback training motivates the performance of SMR-BCI
Duan, X., Xie, S., Lv, Y., Xie, X., Obermayer, K., & Yan, H. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/acaee7 · Cited by 9
Sensorimotor Oscillations During a Reciprocal Touch Paradigm With a Human or Robot Partner
Smyk, N. J., Weiss, S. M., & Marshall, P. J. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02280 · Cited by 6
Effective user training for motor imagery based brain computer interface with object-directed 3D visual display
Liang, S., Choi, K., Qin, J., Pang, W., & Heng, P. (2014) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2014.7002788 · 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
Evaluation of EEG features during overt visual attention during neurofeedback game
Thomas, K. P., Vinod, A. P., & Guan, C. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974188 · Cited by 6
Performance Evaluation of Walking Imagery Training Based on Virtual Environment in Brain-Computer Interfaces
Liu, X., Liang, S., Hang, W., Lei, B., Wang, Q., Qin, J., & Choi, K. (2017) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2017.15 · Cited by 6
POMDP-BCI: A Benchmark of (Re)Active BCI Using POMDP to Issue Commands
Torre, J. J., Chanel, C. P. C., & Dehais, F. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3318578 · Cited by 5
A Brain-Computer Interface Based on Abstract Visual and Auditory Imagery: Evidence for an Effect of Artistic Training
Dhindsa, K., Carcone, D., & Becker, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58625-0_23 · Cited by 5
Brain-Computer Interface Design and Neurofeedback Training in the Case of ADHD Rehabilitation
Sagiadinou, M., & Plerou, A. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-32622-7_19 · Cited by 5
Actual, sham and no-feedback effects in motor imagery practice
Filho, C. A. S., Attux, R., & Castellano, G. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103262 · Cited by 5
Evaluation of Neurofeedback Therapy for Treatment of Central Neuropathic Pain in Paraplegic Patients Using Deep Learning
Saif, M. G. M., Sushkova, L., & Fraser, M. (2023) · SN Computer Science · doi:10.1007/s42979-023-02183-4 · Cited by 4
Performance of stroke patients using a brain-computer interface during motor imagery: a systematic review
Santos, E., Fernandes, C. A., & Castellano, G. (2023) · Research on Biomedical Engineering · doi:10.1007/s42600-023-00284-w · Cited by 4
Connecting Minds and Devices: A Fifty-Year Review of Brain-Computer Interfaces
Sun, Y., Chen, X., Wang, Y., Li, Y., Zhang, Y., Wu, Q., Gao, S., & Gao, X. (2025) · Chinese Journal of Electronics · doi:10.23919/cje.2023.00.325 · Cited by 3
Semiotic Training for Brain-Computer Interfaces
Timofeeva, M. K. (2016) · Annals of Computer Science and Information Systems · doi:10.15439/2016f75 · Cited by 3
A Shaping Procedure to Modulate Two Cognitive Tasks to Improve a Sensorimotor Rhythm-Based Brain-Computer Interface System
da Silva‐Sauer, L., Aguayo, L. V., Velasco-Álvarez, F., Fernández-Rodríguez, Á., & Ron-Angevín, R. (2018) · The Spanish Journal of Psychology · doi:10.1017/sjp.2018.39 · Cited by 3
Evolution of users’ subjective experience over three training sessions with an EEG Motor-Imagery Brain–Computer Interface (MI-BCI)
Roc, A., Kolodzienski, L., Dreyer, P., Appriou, A., Monseigne, T., & Lotte, F. (2025) · Brain Research · doi:10.1016/j.brainres.2025.150085 · Cited by 2
Electroencephalography and Brain–Computer Interfaces
Müller-Putz, G., & Wriessnegger, S. C. (2021) · Springer eBooks · doi:10.1007/978-3-030-68545-4_3 · Cited by 4
Virtual Character Control by Brain-Computer Interface and Comparison of Performance Metrics
Gezgez, C., & Kacar, E. (2021) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista52262.2021.9548604 · Cited by 2
The role of simulation in developing and designing applications for 2-class motor imagery brain-computer interfaces
Quek, M. (2013) · Enlighten: Theses (The University of Glasgow) · Cited by 2
An Empirical Analysis of Neurofeedback Using PID Control Systems
Zeyda, F., Aranyi, G., Charles, F., & Cavazza, M. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.555 · Cited by 1
Skilled Action and the Ethics of Brain-Computer Interfaces
Drosselmeier, S., & Sellmaier, S. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-64590-8_3 · 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
POMDP-BCI: A Benchmark of Active BCI Using POMDP to Issue CommandsPreprint
Torre, J., Dehais, F., & Chanel, C. P. C. (2023) · doi:10.36227/techrxiv.22818212.v1 · Cited by 1
Decoding P300 evoked potentials for Brain Computer Interfaces (BCI) aimed at assisting potential end-users at home
Corralejo, R. (2016) · doi:10.35376/10324/16339
Effects of Interpretation Error on User Learning in Novel Input Mechanisms
Lam, K. C. (2021) · HARVEST (University of Saskatchewan)
Development and Testing of an Eye Gaze and Brain-Computer Interface with Haptic Feedback for Robot Control for Play by Children with Severe Physical Disabilities
Sakamaki, I. (2019) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-b2ac-gh47
Front Matter
Clerc, M., Bougrain, L., & Lotte, F. (2016) · doi:10.1002/9781119144977.fmatter
POMDP-BCI: A Benchmark of Active BCI Using POMDP to Issue CommandsPreprint
Torre, J., Dehais, F., & Chanel, C. P. C. (2023) · doi:10.36227/techrxiv.22818212
Testing Neurofeedback System for Studying Cognitive Tests
Ali, R. (2023) · International Conference on Communication, Computing and Digital Systems (C-CODE) · doi:10.1109/c-code58145.2023.10139862
Electroencephalogram (EEG) Signal Processing
Sharma, R. K., Mishra, S. K., & Fatima, S.-E. (2023) · doi:10.53555/sfs.v10i2.2737
MORPHOLOGICAL CHANGES IN THE PULMONARY HISTIONE IN MINERS AS MARKERS OF EARLY DIAGNOSIS OF PNEUMOCONIOSIS
Бондарев, О. И. (2024) · Scientific Research of the SCO Countries: Synergy and Integration · doi:10.34660/inf.2022.39.79.183
RESEARCH OF THE ORIENTATION ACCURACY OF THE MAGNETIC FIELD OF AN ASYNCHRONOUS MOTOR
Wang, H., & Павлович, В. И. (2024) · Scientific Research of the SCO Countries: Synergy and Integration · doi:10.34660/inf.2022.97.78.196
NeuroCommTrainer: Toward an Adaptive and Wearable Multimodal Brain–Computer Interface
Scherer, J., Finke, A., Everding, V., Lindenbaum, L., Kayser, C., & Kissler, J. M. (2025) · Brain Connectivity · doi:10.1177/21580014251393151
A Novel Approach to Improve SSVEP-BCI Performance Through Neurofeedback Training
Li, M., Yao, Y., Dong, B., Wang, K., Yu, H., Xu, M., & Ming, D. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254803
Towards cognitive brain-computer interfaces : real-time monitoring of visual processing and control using electroencephalography
Gaume, A. (2016) · theses.fr (ABES) · doi:10.70675/53cc1893z83e8z4d77za68ez96adfd9528ee · Cited by 2
Contribution to the study of the use of brain-computer interfaces in virtual and augmented reality
Mercier, J. (2015) · doi:10.70675/3bf15e23z0ecbz4b66zae62z0b1869bc0c0c
Méthodes pour l'électroencéphalographie multi-sujet et application aux interfaces cerveau-ordinateur
Korczowski, L. (2018) · theses.fr (ABES) · doi:10.70675/38d2a12fz024fz4d84z8a8az0dc16b2cff6a
Contribution to the understanding of mental task BCI performances using predictive computational models
Benaroch, C. (2021) · theses.fr (ABES) · doi:10.70675/c2704384z058az4affza403z92acc3ec08f8 · Cited by 1
The partially observable brain : An exploratory study on the use of partially observable Markov decision processes as a general framework for brain-computer interfaces
Torre, J. J. (2024) · theses.fr (ABES) · doi:10.70675/e4e78df0za2b0z4162z8104z7ae752d81a23
Optimization of cVEP Classification Approaches for 3D VR Based Brain Computer Interfaces
Gouret, A., Woelffel, V., Delaux, A., Bars, S. L., & Zilelioglu, H. (2026) · International Conference on Current Research in Artificial Intelligence and Data Science (ICCRAIDS) · doi:10.1109/iccraids67816.2026.11519663
Exploration-based feedback for BCI training: a case study with an adolescent with paraplegia
Ivanov, N., & Chau, T. T. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-02070-y

Authors: Neuper, C., & Pfurtscheller, G.
Affiliations: University of Graz (AT)
Corresponding author: Neuper, Christa
ORCID: Neuper, Christa
Venue: The Frontiers Collection
Publisher: Springer Berlin Heidelberg
Published: 2009
Pages: 65-78
ISSN: 1612-3018 (ISSN-L)
DOI: 10.1007/978-3-642-02091-9_4
OpenAlex: W1891739631
Cited by: 102
References: 68
Citations by year: 2026: 4 · 2025: 5 · 2024: 4 · 2023: 12 · 2022: 3 · 2021: 11 · 2020: 5 · 2019: 7 · 2018: 12 · 2017: 6 · 2016: 8 · 2015: 7 · 2014: 7 · 2013: 6 · 2012: 4
FWCI: 3.20
Type: book chapter
Language: en
Open access: closed
Locations: The frontiers collection (publishedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Keywords: neurofeedback, brain-computer interface, neural signal classification, EEG, electrocorticography, neural decoding, pattern recognition

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

Neurofeedback Training for BCI Control

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks