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High-resolution EEG techniques for brain-computer interface applications
Open accessAbstract
High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.
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“Similar to these previous works, we found that, in general, source features were more correlated with cued motor-related mental states than sensor features (Fig. S9) (44, 45).”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [44]
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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
Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
Hansen, P. C. (1992) · SIAM Review · doi:10.1137/1034115 · Cited by 3,903
BCI2000: 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 3,129
Dale, A. M., & Sereno, M. I. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.162 · Cited by 2,003
Dynamic Statistical Parametric Mapping
Dale, A. M., Liu, A. K., Fischl, B. R., Buckner, R. L., Belliveau, J. W., Lewine, J. D., & Halgren, E. (2000) · Neuron · doi:10.1016/s0896-6273(00)81138-1 · Cited by 1,787
Michel, C., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., & de Peralta, R. G. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.06.001 · Cited by 1,771
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Wolpaw, J. R., & McFarland, D. J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403504101 · Cited by 1,512
Picard, N., & Strick, P. L. (2001) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(01)00266-5 · Cited by 1,184
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
Neocortical Dynamics and Human EEG Rhythms
Nunez, P. L., & Williamson, S. J. (1996) · Physics Today · doi:10.1063/1.2807585 · Cited by 1,005
Research works printed but not linked: 1
- He, 2006, EEG inverse problems, vol. 2, 1355
Cited by
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Pichiorri, F., Morone, G., Petti, M., Toppi, J., Pisotta, I., Molinari, M., Paolucci, S., Inghilleri, M., Astolfi, L., Cincotti, F., et al. (2015) · Annals of Neurology · doi:10.1002/ana.24390 · Cited by 695
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 695
Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives
Yuan, H., & He, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2312397 · Cited by 508
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
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
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control
Edelman, B. J., Meng, J., Suma, D., Zurn, C., Nagarajan, E., Baxter, B. S., Cline, C. C., & He, B. (2019) · Science Robotics · doi:10.1126/scirobotics.aaw6844 · Cited by 380
“These parcels were identified on the basis of seed points assigned to the hand knobs [similar to (44)] by the operators before the experimental session.”
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 351
Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review
Hamedi, M., Salleh, S.-H., & Noor, A. M. (2016) · Neural Computation · doi:10.1162/neco_a_00838 · Cited by 243
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
Wolpaw, J. R. (2013) · Handbook of clinical neurology · doi:10.1016/b978-0-444-52901-5.00006-x · Cited by 231
A Practical Guide to Brain–Computer Interfacing with BCI2000
Schalk, G., & Mellinger, J. (2010) · Springer eBooks · doi:10.1007/978-1-84996-092-2 · Cited by 216
Vecchiato, G., Maglione, A. G., Cherubino, P., Wasikowska, B., Wawrzyniak, A., Latuszynska, A., Latuszynska, M., Nermend, K., Graziani, I., Leucci, M. R., et al. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/912981 · Cited by 164
Self-citationPichiorri, F., Fallani, F. D. V., Febo Cincotti, A., Babiloni, F., Molinari, M., Kleih, S. C., Neuper, C., Kübler, A., & Donatella Mattia, A. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025020 · Cited by 162
Self-citationA novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN
Hou, Y., Zhou, L., Jia, S., & Lun, X. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab4af6 · Cited by 152
Yuan, H., Doud, A., Gururajan, A., & He, B. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2003384 · Cited by 120
Morone, G., Pisotta, I., Pichiorri, F., Kleih, S. C., Paolucci, S., Molinari, M., Cincotti, F., Kübler, A., & Mattia, D. (2015) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2014.05.026 · Cited by 105
Jerbi, K., Vidal, J. R., Mattout, J., Maby, E., Lecaignard, F., Ossandón, T., Hamamé, C. M., Dalal, S. S., Bouet, R., Lachaux, J.-P., et al. (2011) · IRBM · doi:10.1016/j.irbm.2010.12.004 · Cited by 87
Analysis of a commercial EEG device for the control of a robot arm
Ranky, G. N., & Adamovich, S. V. (2010) · IEEE Annual Northeast Bioengineering Conference (NEBEC) · doi:10.1109/nebc.2010.5458188 · Cited by 84
He, B., Gao, S., Yuan, H., & Wolpaw, J. R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5227-0_2 · Cited by 86
Self-citationThe Evolution of Neuroprosthetic Interfaces
Adewole, D. O., Serruya, M. D., Harris, J. P., Burrell, J. C., Petrov, D., Chen, H.-C. I., Wolf, J. A., & Cullen, D. K. (2016) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2016017198 · Cited by 86
An Empirical Bayesian Framework for Brain–Computer Interfaces
Lei, X., Yang, P., & Yao, D. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2027705 · Cited by 80
Materials for Dry Electrodes for the Electroencephalography: Advances, Challenges, Perspectives
Yang, L., Liu, Q., Zhang, Z., Gan, L., Zhang, Y., & Wu, J. (2021) · Advanced Materials Technologies · doi:10.1002/admt.202100612 · Cited by 78
Toda, A., Imamizu, H., Kawato, M., & Sato, M.-A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.057 · Cited by 78
Towards a New Modality-Independent Interface for a Robotic Wheelchair
Bastos-Filho, T., Cheein, F. A., Müller, S. M. T., Celeste, W. C., Cruz, C. D. L., Cavalieri, D. C., Sarcinelli-Filho, M., Amaral, P. F. S., Pérez, E., Soria, C. M., et al. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2265237 · Cited by 76
Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space
Xygonakis, I., Athanasiou, A., Pandria, N., Kugiumtzis, D., & Bamidis, P. D. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7957408 · Cited by 63
Millán, J. D. R. (2013) · Wiley eBooks · doi:10.1002/9781118628522.ch12 · Cited by 61
Brunner, C., Andreoni, G., Bianchi, L., Blankertz, B., Breitwieser, C., Kanoh, S., Kothe, C. A., Lécuyer, A., Makeig, S., Mellinger, J., et al. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5_16 · Cited by 59
Self-citationAdvanced Electrode Technologies for Noninvasive Brain–Computer Interfaces
Lin, S., Jiang, J., Huang, K., Li, L., He, X., Du, P. Y., Wu, Y., Liu, J., Li, X., Huang, Z., et al. (2023) · ACS Nano · doi:10.1021/acsnano.3c06781 · Cited by 54
Strategies for spinal cord repair after injury: a review of the literature and information.
Bauchet, L., Lonjon, N., Perrin, F., Gilbert, C., Privat, A., & Fattal, C. (2009) · Annals of Physical and Rehabilitation Medicine · doi:10.1016/j.annrmp.2008.10.004 · Cited by 55
Network neuroscience for optimizing brain–computer interfaces
Fallani, F. D. V., & Bassett, D. S. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.10.001 · Cited by 47
Yoshimura, N., DaSalla, C. S., Hanakawa, T., Sato, M.-A., & Koike, Y. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.029 · Cited by 46
Evaluation of EEG Features in Decoding Individual Finger Movements from One Hand
Xiao, R., & Ding, L. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/243257 · Cited by 41
Tam, W.-K., Ke, Z., & Tong, R. K. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091566 · Cited by 42
Baxter, B., Edelman, B. J., Sohrabpour, A., & He, B. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00691 · Cited by 41
Rathee, D., Raza, H., Prasad, G., & Cecotti, H. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2726779 · Cited by 44
Leveraging anatomical information to improve transfer learning in brain–computer interfaces
Wronkiewicz, M., Larson, E. B., & Lee, A. K. C. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046027 · Cited by 36
Cognitive load measurement - A methodology to compare low cost commercial EEG devices
Das, R., Chatterjee, D., Das, D., Sinharay, A., & Sinha, A. (2014) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2014.6968528 · Cited by 37
EEG Source Imaging of Movement Decoding: The State of the Art and Future Directions
Handiru, V. S., Vinod, A. P., & Guan, C. (2018) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2017.2778458 · Cited by 34
Akan, O. B., Ramezani, H., Civas, M., Cetinkaya, O., Bilgin, B. A., & Abbasi, N. A. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3056455 · Cited by 28
Motor imagery learning across a sequence of trials in stroke patients
Lee, M., Park, C.-H., Im, C., Kim, J., Kwon, G. H., Kim, L., Chang, W. H., & Kim, Y. (2016) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-150534 · Cited by 23
EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures
Mondini, V., Mangia, A. L., & Cappello, A. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/4562601 · Cited by 24
Vecchiato, G., Toppi, J., Maglione, A. G., Olejarczyk, E., Astolfi, L., Mattia, D., Colosimo, A., & Babiloni, F. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/434296 · Cited by 21
Self-citationMaglione, A. G., Brizi, A., Vecchiato, G., Rossi, D., Trettel, A., Modica, E., & Babiloni, F. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00378 · Cited by 20
Self-citationWriessnegger, S. C., Bauernfeind, G., Kurz, E., Raggam, P., & Müller-Putz, G. (2018) · Brain and Cognition · doi:10.1016/j.bandc.2018.07.006 · Cited by 19
Mondini, V., Mangia, A. L., & Cappello, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193004 · Cited by 19
General principles of machine learning for brain-computer interfacing
Iturrate, I., Chavarriaga, R., & Millán, J. D. R. (2020) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63934-9.00023-8 · Cited by 24
A High Performance MEG Based BCI Using Single Trial Detection of Human Movement Intention
Peter, T., Sharma, K., Holroyd, T., Battapady, H. D., Fei, D.-Y., & Bai, O. (2013) · InTech eBooks · doi:10.5772/54550 · Cited by 20
Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces
Singanamalla, S. K. R., & Lin, C. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.651762 · Cited by 18
Costa, Á., Salazar-Varas, R., Úbeda, A., & Azorín, J. M. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00060 · Cited by 17
Decoding and mapping of right hand motor imagery tasks using EEG source imaging
Edelman, B. J., Baxter, B., & He, B. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146593 · Cited by 17
simBCI—A Framework for Studying BCI Methods by Simulated EEG
Lindgren, J. T., Merlini, A., Lécuyer, A., & Andriulli, F. P. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2873061 · Cited by 17
Toward development of a two-state brain–computer interface based on mental tasks
Faradji, F., Ward, R. K., & Birch, G. E. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/4/046014 · Cited by 16
Rahmouni, L., Mitharwal, R., & Andriulli, F. P. (2017) · Journal of Computational Physics · doi:10.1016/j.jcp.2017.07.013 · Cited by 16
Abijith, V., Kumaar, S., Vishwanath, R. M., & Omkar, S. N. (2018) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2018.00040 · Cited by 16
Liu, H., Zhang, T., Ye, Y., Pan, C., Yang, G., Wang, J., & Qiu, R. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.05289 · Cited by 15
A SSVEP Stimuli Encoding Method Using Trinary Frequency-Shift Keying Encoded SSVEP (TFSK-SSVEP)
Zhao, X., Zhao, D., Wang, X., & Hou, X. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00278 · Cited by 14
“…resolution up to milliseconds while it is cheaper, non-radioactive and provides high portability which makes EEG become a favorable choice when building a BCI system (Sitaram et al., 2007; Cincotti et al., 2008; Athanasiou and Bamidis, 2010; Becedas, 2012; Pistohl et al., 2012; Baranauskas, 2014).”
An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data
Liang, S., Kuang, S., Wang, D., Yuan, Z., Zhang, H., & Sun, L. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3268979 · Cited by 14
Raichur, A., Wihardjo, G., Banerji, S., & Heng, J. (2009) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim.2009.5229760 · Cited by 12
Akan, O., Ramezani, H., Civas, M., Cetinkaya, O., Bilgin, B., Abbasi, N., et al. (2021) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 12
Cortical current density vs. surface EEG for event-related potential-based Brain-Computer Interface
Goel, M. K., Chavarriaga, R., & Millán, J. D. R. (2011) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2011.5910578 · Cited by 12
Giri, A., Kumar, L., & Gandhi, T. K. (2021) · IEEE Sensors Letters · doi:10.1109/lsens.2021.3122453 · Cited by 12
Bastos-Filho, T., Ferreira, A., Cavalieri, D. C., Silva, R., Müller, S. M. T., & Perez, E. (2013) · ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living · doi:10.1109/brc.2013.6487458 · Cited by 11
As above, so below? Towards understanding inverse models in BCI
Lindgren, J. T. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa86d0 · Cited by 9
Alzahrani, S. I., & Anderson, C. W. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721500362 · Cited by 11
Wronkiewicz, M., Larson, E., & Lee, A. K. C. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/5/056017 · Cited by 10
“Manual electrode localization adds about fi ve minutes to the EEG cap fi tting procedure, but photometric methods ( using cameras ) already exist to expedite this process ( Cincotti et al 2008 ) .”
Human machine interface: robotizing the instinctive living
Tahir, A. M. (2018) · International Robotics & Automation Journal · doi:10.15406/iratj.2018.04.00142 · Cited by 10
Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
Cho, J. E., Kang, S. Y., Hong, Y. Y., & Jun, H. J. (2025) · Applied Sciences · doi:10.3390/app15084217 · Cited by 10
“For instance, EEG in gaming or VR environments can detect real-time changes in user state, adjusting interfaces or content dynamically [54–56].”
Node Accessibility in Cortical Networks During Motor Tasks
Chávez, M., Fallani, F. D. V., Valencia, M., Artieda, J., Mattia, D., Latora, V., & Babiloni, F. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9185-2 · Cited by 9
Self-citationEffective brain-computer interfacing using BCI2000
Schalk, G. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5334558 · Cited by 8
Single-trial prediction of reaction time variability from MEG brain activity
Ohata, R., Ogawa, K., & Imamizu, H. (2016) · Scientific Reports · doi:10.1038/srep27416 · Cited by 8
Seeland, A., Krell, M., Straube, S., & Kirchner, E. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00340 · Cited by 6
Soler, A. F., Giraldo, E., & Molinas, M. (2024) · Brain Informatics · doi:10.1186/s40708-024-00224-z · Cited by 7
On a Low-Frequency and Contrast-Stabilized Full-Wave Volume Integral Equation Solver for Lossy Media
Henry, C., Merlini, A., Rahmouni, L., & Andriulli, F. P. (2022) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2022.3161390 · Cited by 7
Development of a Brain Computer Interface (BCI) Speller System Based on SSVEP Signals.
Movahedi, M. M., Mehdizadeh, A., & Alipour, A. (2013) · PubMed Central · Cited by 6
Banerji, S., & Heng, J. (2009) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2009.5209539 · Cited by 6
Garipelli, G., Chavarriaga, R., Febo Cincotti, A., Babilon, F., & Millán, J. D. R. (2009) · Machine learning for signal processing ... · doi:10.1109/mlsp.2009.5306216 · Cited by 6
Qin, Y., Wu, J., Bulger, E., Cao, J., Dehghani, H., Shinn‐Cunningham, B. G., & Kainerstorfer, J. M. (2024) · Biomedical Optics Express · doi:10.1364/boe.531576 · 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
Colamarino, E., Pichiorri, F., Toppi, J., Donatella Mattia, A., & Febo Cincotti, A. (2022) · Brain Topography · doi:10.1007/s10548-021-00883-9 · Cited by 5
Different Perception of Musical Stimuli in Patients with Monolateral and Bilateral Cochlear Implants
Maglione, A. G., Vecchiato, G., Leone, C. A., Grassia, R., Mosca, F., Colosimo, A., Malerba, P., & Babiloni, F. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/876290 · Cited by 4
Self-citationLiu, H., Li, L., Ye, Y., Pan, C., Yang, G., Chen, T., Zhang, T., Wang, J., & Qiu, C. (2023) · Mathematics · doi:10.3390/math11234776 · Cited by 4
Wavelet Based Feature Extraction Scheme ofElectroencephalogram
Kumar, C. E. M., & Kumar, S. V. D. (2014) · International Journal of Innovative Research in Science Engineering and Technology · Cited by 4
Sato, T., Kajiwara, R., Takashima, I., & Iijima, T. (2018) · InTech eBooks · doi:10.5772/intechopen.74810 · Cited by 4
Mellinger, J., & Schalk, G. (2009) · The frontiers collection · doi:10.1007/978-3-642-02091-9_15 · Cited by 4
Preliminary issues on brain - machine contextual communication structure development
Vesselényi, T., Dziţac, I., Dziţac, S., Hora, C., & Porumb, C. (2009) · International Workshop on Soft Computing Applications (SOFA) · doi:10.1109/sofa.2009.5254882 · Cited by 4
Classification of Motor Imagery Tasks in Source Domain
Li, M., Zhang, C., Jia, S., & Sun, Y.-J. (2018) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2018.8484716 · Cited by 4
Digit Recognition System Using EEG Signal
Deore, R. S., Gawali, B. W., & Mehrotra, S. C. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-10978-7_14 · Cited by 3
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Goel, M. K., Chavarriaga, R., & Millán, J. D. R. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122612 · Cited by 2
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Metadata
- Authors
- Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Astolfi, L., De Vico Fallani, F., Tocci, A., Bianchi, L., Marciani, M. G., Gao, S., Millan, J., & Babiloni, F.
- Affiliations
- Fondazione Santa Lucia (IT)
- University of Rome Tor Vergata (IT)
- Tsinghua University (CN)
- Corresponding author
- Babiloni, Fabio
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 167
- Issue
- 1
- Pages
- 31-42
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2098951185
- Cited by
- 115
- References
- 36
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 5 · 2022: 2 · 2021: 7 · 2020: 5 · 2019: 6 · 2018: 12 · 2017: 9 · 2016: 9 · 2015: 8 · 2014: 9 · 2013: 9 · 2012: 4
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- European Commission (BM0601)
- Tsinghua University
- National Institutes of Health (GUP03562)
- National Institute of Biomedical Imaging and Bioengineering (R01EB006356, EB006356)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- high-resolution EEG, brain-computer interface, source localization, individual head model, Tikhonov regularization, motor imagery, sensorimotor rhythm, spectral properties, online BCI, distributed source model, current dipoles, Brodmann areas
- MeSH terms
- Adult, Biofeedback, Psychology, Brain, Brain Mapping, Communication Devices for People with Disabilities, Electrodes, Electroencephalography, Evoked Potentials, Somatosensory, Female, Humans, Male, Online Systems, Signal Processing, Computer-Assisted, User-Computer Interface, Evoked Potentials, Motor
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