Spatial patterns underlying population differences in the background EEG

Introduction to Statistical Pattern Recognition

(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,023

Introduction to Statistical Pattern Recognition

Webb, A. R., & Copsey, K. D. (2011) · doi:10.1002/9781119952954.ch1 · Cited by 1,607

Principles of spatial analysis

Lehmann, D. (1987) · Medical Entomology and Zoology · Cited by 494

Scalp Current Density Mapping: Value and Estimation from Potential Data

Perrin, F., Bertrand, O., & Pernier, J. (1987) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1987.326089 · Cited by 425

Significance probability mapping: An aid in the topographic analysis of brain electrical activity

Duffy, F. H., Bartels, P. H., & Burchfiel, J. L. (1981) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(81)90221-2 · Cited by 354

Quantitative EEG: I. Techniques and problems of frequency analysis and topographic mapping.

Nuwer, M. R. (1988) · PubMed · Cited by 254

Quantitative EEG

Nuwer, M. R. (1988) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-198801000-00001 · Cited by 247

Analysis of the Electromagnetic Signals of the Human Brain: Milestones, Obstacles, and Goals

Gevins, A. S. (1984) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1984.325246 · Cited by 127

Quantitative EEG

Nuwer, M. R. (1988) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-198801000-00002 · Cited by 114

Handbook of Electroencephalography and Clinical Neurophysiology

Roth, B. (1977) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(77)90272-3 · Cited by 88

Information based source number estimation for probabilistic common spatial pattern in motor imagery BCI system

Zarei, A. A., Ghassemi, F., & Moradi, M. H. (2016) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2016.7585584 · Cited by 1

Machine Learning Reveals Different Brain Activities in Visual Pathway during TOVA Test

Sun, H., Sourina, O., Yang, Y., Huang, G.-B., Denk, C., & Klanner, F. (2014) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-319-14063-6_22 · Cited by 1

Classifying EEG motor imagery signals using supervised projection pursuit for artefact removal

Grear, T. J., & Jacobs, D. J. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9659169 · Cited by 1

A pain assessment method based on decoding local field potential signals

Cui, Y., Zhang, B., Gao, P., & Zhao, J. (2024) · WRC Symposium on Advanced Robotics and Automation (WRC SARA) · doi:10.1109/wrcsara64167.2024.10685820 · Cited by 1

Invariant common spatial pattern advanced feature extraction in mu rhythms of EEG signals

Nguyen, T.-H., Park, S.-M., Ko, K.-E., & Sim, K.-B. (2012) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2012.6388513 · Cited by 1

A Robust Feature Set for Wearable Multichannel Myoelectric Devices in Practice

Lu, H., Zhang, H., Lin, M., & Wang, R. (2016) · International Conference on Digital Home (ICDH) · doi:10.1109/icdh.2016.049 · Cited by 1

Scalable Motor Movement Recognition from Electroencephalography using Machine Learning

Sharma, A., Singh, S., Wright, B., Perry, A., Woodbridge, D. M.-K., & Popa, A. M. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.10253 · Cited by 1

Multiband Based Joint Sparse Representation for Motor Imagery Classification

Yin, X. X., & Meng, M. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-6554-7_34 · Cited by 1

A new common spatial pattern-based unified channels algorithm for driver’s fatigue EEG signals classification

Zeng, H., & Zakaria, W. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07833-x · Cited by 1

Localizing hidden regularities with known temporal structure in the EEG evoked response dataPreprint

Kuznetsova, A., Krugliakova, E., & Ossadtchi, A. E. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/093922 · Cited by 1

Practical electroencephalography (EEG) applications in stroke rehabilitation: Towards brain-computer interface (BCI) setup and motor function assessment

Zhang, X. (2019) · Summit (Simon Fraser University) · Cited by 1

A SPA-based Manifold Learning Framework for Motor Imagery EEG Data ClassificationPreprint

Li, X., Chen, P., & Bao, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.00865 · Cited by 1

Local Subspace Pruning (LSP) for Multichannel Data DenoisingPreprint

Alain De Cheveigné, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.27.482148 · Cited by 1

Mixing hand-crafted and learned features for EEG-based motor imagery classification

Bustios, P. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2024.tde-10012025-160708 · Cited by 1

The Rayleigh Quotient and Contrastive Principal Component Analysis IPreprint

Carilli, M., Jackson, K. C., & Pachter, L. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.19.689125 · Cited by 1

Classification of Electro-encephalographic Spatial Patterns

Müller, T., Tonio Ball, A., Kristeva-Feige, R., Mergner, T., & Timmer, J. (2000) · Lecture notes in computer science · doi:10.1007/3-540-39949-6_9

Feature extraction for BCIs based on electromagnetic source localization and Common Spatial Patterns

Zaitcev, A., Cook, G. G., Liu, W., Paley, M. N. J., & Milne, E. (2015) · European Conference on Antennas and Propagation

Optimal Local Temporal Common Spatial Patterns for Single-trial EEG Classification

Huang, X., & Zheng, W. (2009) · WRI Global Congress on Intelligent Systems · doi:10.1109/gcis.2009.152

Localisation of brain functions : stimuling brain activity and sourcereconstruction for classification/

Noirhomme, Q. (2006) · DIAL (Catholic University of Leuven)

Mise en évidence des potentiels évoqués par CSP pour le dispositif d'interface P300 Speller

Boudet, S., Peyrodie, L., Gallois, P., & Vasseur, C. P. A. (2007)

EEG-fMRI integration for identification of active brain regions using sparse source decomposition

Samadi, S. (2014) · theses.fr (ABES) · doi:10.70675/bb504d46z7cbbz42d1za724zcc9fa1b2c396

Classicatio no fElectro-encephalographic Spatia lPatterns

Müller, T., Tonio Ball, A., Kristeva-Feige, R., & Timmer, J. (2000)

Conjuntos K de redes neurais e sua aplicação na classificação de imagética motora

Piazentin, D. R. D. M. (2014) · doi:10.11606/d.55.2014.tde-10022015-203830

Frequency peak features for low-channel classification in motor imagery paradigms

Jayaram, V., Schölkopf, B., & Grosse‐Wentrup, M. (2017) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2017.8008355

Performane of partial directed coherence subject to volume consuction effects.

Rincón, D. C. G. (2017) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2017.tde-12072017-131157

Seleção de bandas de frequência na classificação de eletroencefalogramas de imagética motora

Bustios, P. (2017) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2017.tde-21092017-165153

Simultaneous bio-signal measurement system for multiple users — development and validation

Lee, S., Cho, H., & Jun, S. C. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282128

Influence Analysis of Common Spatial Pattern Properties to BCI Accuracy

Plechawska–Wójcik, M., & Zapała, D. (2018) · Conference on Human System Interactions · doi:10.1109/hsi.2018.8431214

Detecting the no-control state in self-paced Brain-Computer Interfaces

Stoica, M. (2012)

Novel Model Architecture for EEG Emotion Classification

Christensen, L. R., & Abdullah, M. A. (2019) · International Journal of Biotech Trends and Technology · doi:10.14445/22490183/ijbtt-v9i3p601

Heterogeneous Label Space Transfer Learning for Brain-Computer Interfaces: A Label Alignment ApproachPreprint

He, H., & Wu, D. (2019) · arXiv (Cornell University)

Exploiting Generalized Matched Filter for Efficient and Effective P300 Detection in Speller Paradigm

Liu, H., Li, Y., & Wang, S. (2019) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3373509.3373544

Emotion-robust EEG Classification for Motor ImageryPreprint

Moeed, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.13523

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

Study of Interactive Systems Based on Brain and Computer InterfacesPreprint

Ghadiri, Z. (2019) · doi:10.31224/osf.io/qg8er

Classification of Movement-Related Oscillations in sEEG Recordings with Machine LearningPreprint

Rockhill, A. P., Mantovani, A., Stedelin, B., Raslan, A. M. T., & Swann, N. C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.28.486094

Identifying Patients with Epilepsy Having Depression/Anxiety Disorder Using Common Spatial Patterns of Functional EEG Networks

Zhang, Y., Lin, Y., Qu, Z., Zhang, Z., Guo, D., Chen, X., Shu, H., & Yu, S. (2022) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-022-00726-3

Covariance-based decoding reveals content-specific feature integration and top-down processing during visual imageryPreprint

Mantegna, F., Olivetti, E., Schwedhelm, P., & Baldauf, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.26.509536

Investigating the addition of singing imagery as a control task in motor imagery BCI

Mohammadpour, H., & Power, S. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945386

Evaluation of different deep learning approaches for EEG classification

Scharnagl, B., & Groth, C. (2022) · International Conference on Artificial Intelligence for Industries (AI4I) · doi:10.1109/ai4i54798.2022.00018

Autism Detection in Children using Common Spatial Patterns of MEG Signals

Barik, K., Watanabe, K., Hirosawa, T., Yoshimura, Y., Kikuchi, M., Bhattacharya, J., & Saha, G. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340449

Biohybrid Technology for the Detection of Ultralow Concentrations of Trinitrotoluene in Air

Kiroy, V. N., Kosenko, P. O., Shepelev, I. E., Щербань, И. В., Smolikov, A. B., Arsenyev, F. V., Zaborovsky, A. V., Aksenov, V. A., Tivileva, M. I., Gruznov, V. M., et al. (2023) · Journal of Analytical Chemistry · doi:10.31857/s0044450223080091

Msrtnet: Multi-Scale Spatial Residual Network Based on Time-Domain TransformerPreprint

Xin, G., Zhang, D., Wu, X., & Metcalfe, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4886628

Olfactory Paradigm for Reactive Brain-Computer Interface: EEG Response Spatial Visualization and Clustering

Kasprzak, H., Niewińska, N., Komendziński, T., Otake, M., & Rutkowski, T. M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650010

A Compact Convolutional Neural Network for Decoding EEG Functional Connectivity: Application to Motor Imagery

Borra, D., Diciotti, S., & Magosso, E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82487-6_8

Enhancing Cross-session Motor Imagery Classification using Graphical Models in ALS PatientsPreprint

Patel, R. J., Bryson, J. B., Carlson, T., Jiang, D., & Demosthenous, A. (2025) · doi:10.36227/techrxiv.175099877.70777295/v1

EEG-based motor execution classification of upper and lower extremities using machine learning

Korkmaz, I., & Tepe, C. (2025) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2025.2566260

Prediction of hypnotic trance with brain-evoked responses to an auditory oddball using magnetoencephalography

Sid-Ahmed, H., Alayrangues, J., Langar, L., Richard, N., Albaladejo, V., Pezzani, S., Brun, V., Anglade, D., & Auboiroux, V. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253045

varEEGNet: An Improved Neural Network for Motor Imagery Based BCI

Darley, G., & Bonnet, S. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226211

Additional file 1 of Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

Ferrero, L., Soriano-Segura, P., Navarro, J., Jones, O., Ortiz, M., Iáñez, E., Azorín, J. M., & Contreras-Vidal, J. L. (2024) · Figshare · doi:10.6084/m9.figshare.26702773

Analyzing and improving generalization over time in CSP-based Brain-Computer Interfaces

Haanstra, S. (2008)

MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI

Ballı, T. (2025) · Uludağ University Journal of The Faculty of Engineering · doi:10.17482/uumfd.1698204

Efficient multi-class motor imagery EEG classification with cosine convolution and channel-group spatial feature extraction

Zhang, R., Wang, Y., Zhou, W., & Liu, G. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110226

Interprétation des signaux cérébraux pour l’autonomie des handicapés : Système de reconnaissance de mots imaginés

Abdallah, N. (2018) · doi:10.70675/ee663f39zbf84z428aza78ezed20d70bcee2

Deep learning methods for motor imagery detection from raw EEG : applications to brain-computer interfaces

Avilov, O. (2021) · theses.fr (ABES) · doi:10.70675/e88205f1zf2bfz4c21z999dza3a216d76cdc

Enabling real-world EEG applications with deep learning

Banville, H. J. (2022) · theses.fr (ABES) · doi:10.70675/12e08edaz0021z43fbzb6adz07d315fc6ee2

Transfert d'apprentissage intra et inter sujets en interfaces cerveau-machine non-invasives

Bleuzé, A. (2023) · theses.fr (ABES) · doi:10.70675/df3eca83z03adz47dbz88cczb6c306c34ae3

MCFANet: a multi-class fusion attention network for motor imagery EEG classification

Zhao, P., Liang, T., Jia, H., Dayan, A., Dinarès-Ferran, J., & Solé‐Casals, J. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1811759

Quantized Compact 3D-CNN with High Classification Accuracy in Motor Imagery Brain-Computer Interface

Kobayashi, N., & Ino, M. (2025) · International Conference on Emerging Innovation and Digital Technology (ICEIDT) · doi:10.1109/iceidt66693.2025.11473525

Improving motor imagery based BCI through metric learning

Ballı, T., & Yetkin, E. F. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110460

MI recognition by subject specific localised frequency fusion

Rahman, M. K. M., & Shuvo, H. M. T. (2026) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2026.2720510

A generalizable OPM-MEG framework for time-resolved language decoding during natural speech production

Xu, Y., Zhang, Y., Peng, Y., Yan, B., Wu, Z., Ding, M., & Chen, L. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122219 · Cited by 2

Residual graph convolutional network for motor imagery EEG signal classification based on transformer-enhanced features

Zhao, Z., Tao, Q., Qu, S., Tang, Y., Ma, L., Pei, H., & Zhu, Y. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116596

Design of an adaptive lower limb rehabilitation system with virtual reality and multimodal biosignals

Kuang, X., Yin, Z., Wang, X., & Ouyang, H. (2026) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2026-0521

Spatial patterns underlying the human electroencephalogram

Koles, Z. J., Lazar, M. S., Kasmia, A., & Zhou, Z. (2003) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.1989.96190 · Cited by 2

A method for classifying mental tasks in the space of EEG transforms

Amorim, R., Mirkin, B., & Gan, J. (2009) · Open Access at Essex (University of Essex)

On Some Methods for Eeg Pattern Discrimination

Satô, K., Ono, K., Chiba, G., & Fukata, K. (1977) · International Journal of Neuroscience · doi:10.3109/00207457709147210 · Cited by 11

Computer-assisted interpretation of clinical EEGs

Binnie, C. D., Batchelor, B. G., Bowring, P. A., Darby, C., Herbert, L., Lloyd, D. S. L., Smith, D. M., Smith, G. F., & Smith, M. B. (1978) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(78)90125-6 · Cited by 41

Asymptotic Spatiotemporal Averaging of the Power of EEG Signals for Schizophrenia Diagnostics

Duch, W., Tołpa, K., Ratajczak, E. K., Hajnowski, M., Furman, Ł., & Alexandre, L. A. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8138-0_34 · Cited by 2