Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965 · see citing works

A Global Geometric Framework for Nonlinear Dimensionality Reduction

Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · see citing works

Analysis of a complex of statistical variables into principal components.

Hotelling, H. (1933) · Journal of Educational Psychology · doi:10.1037/h0071325 · Cited by 9,691 · see citing works

Brain–computer interfaces for communication and control

Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927 · see citing works

Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

Belkin, M., & Niyogi, P. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0080 · Cited by 4,534 · see citing works

Advanced engineering mathematics

(1998) · Choice Reviews Online · doi:10.5860/choice.35-5129 · Cited by 4,102 · see citing works

Locality Preserving Projections

He, X., & Niyogi, P. (2003) · Cited by 3,942 · see citing works

A combined SVM and LDA approach for classification

Xiong, T., & Cherkassky, V. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556089 · Cited by 984 · see citing works

Fundamentals of Anatomy and Physiology

Martini, F. H. (1995) · Medical Entomology and Zoology · Cited by 477 · see citing works

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

(2005) · doi:10.1109/ijcnn10471.2005 · Cited by 426 · see citing works

Fundamentals of Anatomy and Physiology

Chruścik, A., Kauter, K., Windus, L. C. E., Whiteside, E., & Dooley, L. (2023) · University of Southern Queensland ePrints (University of Southern Queensland) · doi:10.26192/q6v2w · Cited by 417 · see citing works

Neurophysiologic Basis of EEG

Olejniczak, P. W. (2006) · Journal of Clinical Neurophysiology · doi:10.1097/01.wnp.0000220079.61973.6c · Cited by 295 · see citing works

A brain-controlled switch for asynchronous control applications

Mason, S. G., & Birch, G. E. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.871402 · Cited by 293 · see citing works

Feature extraction for classification problems and its application to face recognition

Kwak, N. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.012 · Cited by 164 · see citing works

Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters

Farina, D., do Nascimento, O. F., Lucas, M.-F., & Doncarli, C. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.01.011 · Cited by 108 · see citing works

Movement-related parameters modulate cortical activity during imaginary isometric plantar-flexions

do Nascimento, O. F., Dremstrup, K., & Voigt, M. (2005) · Experimental Brain Research · doi:10.1007/s00221-005-0247-z · Cited by 83 · see citing works

HMM and IOHMM Modeling of EEG Rhythms for Asynchronous BCI Systems

Chiappa, S., & Bengio, S. (2004) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 82 · see citing works

Movement-related potentials with reference to isometric force output in discrete and repetitive tasks

SLOBOUNOV, S. M., & Ray, W. J. (1998) · Experimental Brain Research · doi:10.1007/s002210050590 · Cited by 66 · see citing works

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS)

Lati̇foğlu, F., Polat, K., Kara, S., & Güneş, S. (2007) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2007.04.001 · Cited by 59 · see citing works

The international 10–20 system revisited: Cartesian and spherical co-ordinates

Böcker, K. B. E., van Avermaete, J. A. G., & van den Berg-Lenssen, M. M. C. (1994) · Brain Topography · doi:10.1007/bf01187714 · Cited by 48 · see citing works

Local neural classifier for EEG-based recognition of mental tasks

Millán, J. D. R., Mourino, J., Babiloni, F., Febo Cincotti, A., Varsta, M., & Heikkonen, J. (2000) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2000.861393 · Cited by 48 · see citing works

Brain interface research for asynchronous control applications

Borisoff, J., Mason, S. G., & Birch, G. E. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875526 · Cited by 46 · see citing works

Classification of neuronal spikes over the reconstructed phase space

Chan, H., Wu, T., Lee, S., Fang, S., Chao, P.-K., & Lin, M.-A. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.09.017 · Cited by 20 · see citing works

A New Design of the Asynchronous Brain Computer Interface Using the Knowledge of the Path of Features

Bashashati, A., Ward, R. K., & Birch, G. E. (2005) · doi:10.1109/cne.2005.1419563 · Cited by 14 · see citing works

Brain Computer Interfaces, a Review

Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143 · see citing works

Detection of movement intention from single-trial movement-related cortical potentials

Niazi, I. K., Jiang, N., Tiberghien, O., Nielsen, J. F., Dremstrup, K., & Farina, D. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/6/066009 · Cited by 259 · see citing works

EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey

Hwang, H.-J., Kim, S., Choi, S., & Im, C.-H. (2013) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2013.780869 · Cited by 257 · see citing works

A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials

Shakeel, A., Navid, M. S., Anwar, M. N., Mazhar, S., Jochumsen, M., & Niazi, I. (2015) · Comput. Math. Methods Medicine · doi:10.1155/2015/346217 · Cited by 149 · see citing works

A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges

Yadav, D., Yadav, S., & Veer, K. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108918 · Cited by 118 · see citing works

Decoding Movement From Electrocorticographic Activity: A Review

Volkova, K., Lebedev, M., Kaplan, A., & Ossadtchi, A. E. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00074 · Cited by 108 · see citing works

Mind the gap: State-of-the-art technologies and applications for EEG-based brain–computer interfaces

Lara, R. P., Tahirbegi, I. B., Chapman, C. A. R., Goding, J. A., & Green, R. A. (2021) · APL Bioengineering · doi:10.1063/5.0047237 · Cited by 83 · see citing works

Survey on Brain-Computer Interface

Bablani, A., Edla, D. R., Tripathi, D., & Cheruku, R. (2019) · ACM Computing Surveys · doi:10.1145/3297713 · Cited by 79 · see citing works

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

Jochumsen, M., Niazi, I., Dremstrup, K., & Kamavuako, E. (2015) · Medical and Biological Engineering and Computing · doi:10.1007/s11517-015-1421-5 · Cited by 74 · see citing works

Noninvasive Brain–Machine Interfaces for Robotic Devices

Tonin, L., & Millán, J. D. R. (2020) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-012720-093904 · Cited by 66 · see citing works

Brain computer interface: A comprehensive survey

Tiwari, N., Edla, D. R., Dodia, S. J., & Bablani, A. (2018) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2018.10.005 · Cited by 57 · see citing works

A review of electroencephalogram signal processing methods for brain-controlled robots

Huang, Z., & Wang, M. (2021) · Cognitive Robotics · doi:10.1016/j.cogr.2021.07.001 · Cited by 49 · see citing works

Brain Control: Human-computer Integration Control Based on Brain-computer Interface Approach

Wang, X., Jin, J., Zhang, Y., & Bei, W. A. N. G. (2013) · Acta Automatica Sinica · doi:10.1016/s1874-1029(13)60023-3 · Cited by 44 · see citing works

Brain-Computer Interface Speller System for Alternative Communication: A Review

Kundu, S., & Ari, S. (2021) · IRBM · doi:10.1016/j.irbm.2021.07.001 · Cited by 42 · see citing works

Deep learning EEG response representation for brain computer interface

Liu, J., Yin, C., & Weidong, Z. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7260182 · Cited by 38 · see citing works

Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)

Shahini, N., Bahrami, Z., Sheykhivand, S., Marandi, S., Danishvar, M., Danishvar, S., & Roosta, Y. (2022) · Electronics · doi:10.3390/electronics11203297 · Cited by 30 · see citing works

EEG-Based Brain-Computer Interfacing (BCI)

Bansal, D., & Mahajan, R. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814687-3.00002-8 · Cited by 29 · see citing works

Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients

Kamavuako, E. N., Jochumsen, M., Niazi, I. K., & Dremstrup, K. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/858015 · Cited by 26 · see citing works

An Accurate, Versatile, and Robust Brain Switch for Neurorehabilitation

Jiang, N., Mrachacz‐Kersting, N., Xu, R., Dremstrup, K., & Farina, D. (2014) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-09979-8_5 · Cited by 23 · see citing works

Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors

Liu, D., Chen, W., Chavarriaga, R., Pei, Z., & Millán, J. D. R. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00560 · Cited by 22 · see citing works

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 22 · see citing works

Evaluation of ANN, LDA and Decision trees for EEG based Brain Computer Interface

Ishfaque, A., Awan, A. J., Rashid, N., & Iqbal, J. (2013) · International Conference on Emerging Technologies · doi:10.1109/icet.2013.6743513 · Cited by 18 · see citing works

IterativeN-way partial least squares for a binary self-paced brain–computer interface in freely moving animals

Eliseyev, A., Moro, C., Costecalde, T., Torres-Martinez, N., Gharbi, S., Mestais, C., Benabid, A. L., & Aksenova, T. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/4/046012 · Cited by 17 · see citing works

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 · see citing works

Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges

Chakladar, D. D., & Chakraborty, S. (2018) · Unsupervised and semi-supervised learning · doi:10.1007/978-3-319-98566-4_5 · Cited by 17 · see citing works

Motor execution detection based on autonomic nervous system responses

Marchal–Crespo, L., Zimmermann, R., Lambercy, O., Edelmann, J., Fluet, M.-C., Wolf, M., Gassert, R., & Riener, R. (2012) · Physiological Measurement · doi:10.1088/0967-3334/34/1/35 · Cited by 16 · see citing works

A Study on Brain–Computer Interface: Methods and Applications

Kumari, A., & Edla, D. R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01515-0 · Cited by 14 · see citing works

Principal component analysis in combination with case-based reasoning for detecting therapeutically correct and incorrect measurements in continuous glucose monitoring systems

Leal, Y., Ordóñez, M. R., Lorencio, C., Bondía, J., Delgado, L. E. M., & Vehı́, J. (2013) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2013.05.008 · Cited by 14 · see citing works

A space-frequency localized approach of spatial filtering for motor imagery classification

Rahman, M. K. M., & Joadder, M. A. M. (2020) · Health Information Science and Systems · doi:10.1007/s13755-020-00106-8 · Cited by 12 · see citing works

Detection of Movement Intentions through a Single Channel of Electroencephalography

Jochumsen, M., Niazi, I., Rovsing, H., Rovsing, C., Nielsen, G. A. R., Andersen, T., Dong, N., Sørensen, M. E., Mrachacz-Kersting, N., Jiang, N., et al. (2014) · Biosystems & biorobotics · doi:10.1007/978-3-319-08072-7_69 · Cited by 13 · see citing works

Fast and Efficient Four‑class Motor Imagery Electroencephalography Signal Analysis Using Common Spatial Pattern– Ridge Regression Algorithm for the Purpose of Brain– Computer Interface

Rezaei, M., Seifzadeh, S., Karim Faez, A., & Amiri, M. (2017) · Journal of Medical Signals & Sensors · doi:10.4103/2228-7477.205593 · Cited by 11 · see citing works

Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction

Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works

A Machine Learning Study of Anxiety-related Symptoms and Error-related Brain Activity

Grabowska, A., Sondej, F., & Senderecka, M. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02126 · Cited by 6 · see citing works

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 · see citing works

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 · see citing works

Detection of movement-related cortical potentials associated with emergency and non-emergency tasks

Li, S., Zhu, Y., Huang, G., Zhang, L., Zhang, Z., & Huang, K. (2018) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2018.8631694 · Cited by 4 · see citing works

Blind Source Separation Based of Brain Computer Interface System: A review

Abdullah, A. K., Abdullah, A. K., & Zhang, Z. (2014) · Research Journal of Applied Sciences Engineering and Technology · doi:10.19026/rjaset.7.280 · Cited by 3 · see citing works

New applications of multimodal human-computer interfaces

Czyżewski, A. (2012) · Signal Processing: Algorithms, Architectures, Arrangements, and Applications · Cited by 1 · see citing works

Clasificación de características de electroencefalogramas en sistemas Brain Computer Interface basados en ritmos sensoriomotores

Alonso, N., & Fernando, L. (2012) · UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) · Cited by 1 · see citing works

Analysis of Movement-Related Cortical Potentials for Brain-Computer Interfacing in Stroke Rehabilitation

Jochumsen, M. (2015) · VBN Forskningsportal (Aalborg Universitet) · doi:10.5278/vbn.phd.med.00007

Short-Latency Brain-Computer Interface Using Movement-Related Cortical Potentials

Xu, R. (2016) · doi:10.53846/goediss-5770

Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Hamedi, M. (2016)

Data Classification Through Cognitive Computing

Chakraborty, S., & Dey, L. (2023) · Data-intensive research · doi:10.1007/978-981-19-8004-6_6

Bayesian signal detection and source separation in simulated brain computer interface systems

Mubeen, M. A. (2016) · doi:10.54014/167k-by1z

An Analysis of Optimization Techniques to Explore the Possibilities in Brain-computer Interfaces: Mindful Machines

Manimegalai, R., & Sukanya, N. S. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815324365125030008

Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential

Mesin, L., Ghani, U., & Niazi, I. K. (2023) · Electronics · doi:10.3390/electronics12051246 · Cited by 6 · see citing works

Muscles movement intention detection from EEG using movement related cortical potentials (MRCPs)

Hussain, M., Bhatti, K. A., & Zaidi, T. (2017) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2017.8388670 · Cited by 2 · see citing works

ORIGINAL ARTICLE Detection of movement-related cortical potentials based on subject-independent training

Khan, I., Ning, N., Jochumsen, J. M., Feldbæk, J., & Nielsen, K. D. (2016)

Detection of Movement Related Cortical Potentials from EEG Using Constrained ICA for Brain-Computer Interface Applications

Karimi, F., Kofman, J., Mrachacz‐Kersting, N., Farina, D., & Jiang, N. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00356 · Cited by 58 · see citing works

Detection of Movement Related Cortical Potentials from Single Trial EEG Signals

Aydın, E. A. (2022) · Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji · doi:10.29109/gujsc.1083912 · Cited by 4 · see citing works