The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Yaşlı Kadınlarda Üreme Sağlığı

Kokanalı, D., & Engin Üstün, Y. (n.d.) · DergiPark (Istanbul University) · Cited by 170,090

Two new Later Stone Age sites from the Final Pleistocene in the Falémé Valley, eastern Senegal

Ndiaye, M., Lespez, L., Tribolo, C., Rassé, M., Hadjas, I., Davidoux, S., Huysecom, É., & Douze, K. (2024) · Monash University Research Portal (Monash University) · doi:10.3929/ethz-b-000667478 · Cited by 24,214

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

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

EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis

Klimesch, W. (1999) · Brain Research Reviews · doi:10.1016/s0165-0173(98)00056-3 · Cited by 6,889

Actual causes of death in the United States

McGinnis, J. M. (1993) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1993.03510180077038 · Cited by 5,480

Actual Causes of Death in the United States, 2000

Mokdad, A. H. (2004) · JAMA · doi:10.1001/jama.291.10.1238 · Cited by 5,211

Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential

Leski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PubMed · doi:10.18154/rwth-conv-084442 · Cited by 3,667

Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.

Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228

A review of classification algorithms for EEG-based brain–computer interfaces

Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665

Performance comparison of a non-invasive P300-based BCI mouse to a head-mouse for people with SCI

Dutt-Mazumder, A., & Huggins, J. E. (2020) · Brain-Computer Interfaces · doi:10.1080/2326263x.2020.1716532 · Cited by 10

Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface

Wang, S., Luo, Z., Zhao, S., Zhang, Q., Liu, G., Wu, D., Yin, E., & Chen, C. (2023) · Bioengineering · doi:10.3390/bioengineering11010030 · Cited by 10

Evaluating Typing Performance in Different Mixed Reality Manifestations using Physiological Features

Chiossi, F., Khaoudi, Y. E., Ou, C., Sidenmark, L., Zaky, A., Feuchtner, T., & Mayer, S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3698142 · Cited by 10

The Fractal Analysis of the Images and Signals in Medical Diagnostics

Albertovich, T. D., & Aleksandrovna, R. I. (2017) · InTech eBooks · doi:10.5772/intechopen.68167 · Cited by 10

A Multi-Modal Neuro-Physiological Study of Malicious Insider Threats

Hashem, Y., Takabi, H., Dantu, R., & Nielsen, R. D. (2017) · doi:10.1145/3139923.3139930 · Cited by 13

Classification of BCI Users Based on Cognition

Özkan, N. F., & Kahya, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6315187 · Cited by 10

Improved Brain–Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery

Wang, F., Liu, H., Zhao, L., Su, L., Zhou, J., Gong, A., & Fu, Y. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.880304 · Cited by 10

Symbiotic Interaction

Gamberini, L., Gamberini, L., Spagnolli, A., Jacucci, G., Blankertz, B., & Freeman, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57753-1 · Cited by 11

Mapping Intrinsic and Extrinsic Muscle Myoelectric Activity During Natural Dynamic Movements Into Finger and Wrist Kinematics Using Deep Learning Prediction Models

Panchal, M., Tanzarella, S., Jung, M., & Farina, D. (2023) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2023.3302613 · Cited by 10

Imagined Speech Detection Using Multi-Receptive CNN for Asynchronous BCI Communication and Neurorehabilitation

Ko, B.-K., Lee, S., & Lee, S. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3592312 · Cited by 11

Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex

Min, B., Kim, H.-S., Ko, W., Ahn, M.-H., Suk, H., Pantazis, D., & Knight, R. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118165 · Cited by 10

Les interfaces cerveau-ordinateur. Conception et utilisation en réalité virtuelle

Lotte, F. (2012) · Techniques et sciences informatiques · doi:10.3166/tsi.31.289-310 · Cited by 9

Brain-computer interface: a thought translation device turning fantasy into reality

Tyagi, A., & Nehra, V. (2013) · International Journal of Biomedical Engineering and Technology · doi:10.1504/ijbet.2013.055044 · Cited by 9

The effect of distractors on SSVEP-based brain-computer interfaces

Zerafa, R., Camilleri, T. A., Camilleri, K. P., & Falzon, O. (2019) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ab155d · Cited by 9

Real-Time EEG-Based BCI for Self-Paced Motor Imagery and Motor Execution Using Functional Neural Networks

Heim, M., Heinrichs, F., Hueppe, M., Nunez, F., Szameitat, A., Reuter, M., Goetz, S. M., & Weber, C. (2025) · IEEE Access · doi:10.1109/access.2025.3569932 · Cited by 8

A Passive Brain-Computer Interface for Predicting Pilot Workload in Virtual Reality Flight Training

van Weelden, E., van Beek, C. W. E., Alimardani, M., Wiltshire, T. J., Ledegang, W. D., Groen, E. L., & Louwerse, M. M. (2024) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms59971.2024.10555679 · Cited by 8

When brain and behavior disagree: Tackling systematic label noise in EEG data with machine learning

Porbadnigk, A. K., Görnitz, N., Sannelli, C., Binder, A., Braun, M. L., Kloft, M., & Müller, K. (2014) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2014.6782561 · Cited by 8

Passive Brain–Computer Interfaces

George, L., & Lécuyer, A. (2014) · Springer eBooks · doi:10.1007/978-1-4471-6584-2_13 · Cited by 8

An ensemble classification approach to motor-imagery brain state discrimination problem

Datta, A., Chatterjee, R., Sanyal, D. K., & Guha, D. (2017) · International Conference on Infocom Technologies and Unmanned Systems (ICTUS) · doi:10.1109/ictus.2017.8286026 · Cited by 8

Embedded EEG Recording Module with Active Electrodes for Motor Imagery Brain-Computer Interface

Mercado-Gutierrez, J. A., Herrera, J., Pansza, A. D. J., & Gutiérrez-Martínez, J. (2016) · IEEE Latin America Transactions · doi:10.1109/tla.2016.7437185 · Cited by 8

A New Experimental Paradigm for Affective Research in Neuro-adaptive Technologies

Pollmann, K., Ziegler, D., Peißner, M., & Vukelić, M. (2017) · doi:10.1145/3038439.3038442 · Cited by 8

A Review of Brain-Computer Interface

Paul, D., Mukherjee, M., & Bakshi, A. (2021) · Lecture notes in bioengineering · doi:10.1007/978-981-33-6915-3_50 · Cited by 8

Real-time Detection of Driver’s Movement Intention in Response to Traffic LightsPreprint

Khaliliardali, Z., Chavarriaga, R., Zhang, H., Gheorghe, L., Perdikis, S., & Millán, J. D. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/443390 · Cited by 8

EEG-based neuroergonomics for 3d user interfaces: opportunities and challenges

Frey, J., Hachet, M., & Lotte, F. (2017) · Le travail humain · doi:10.3917/th.801.0073 · Cited by 12

Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control

Lee, B.-H., Cho, J.-H., Kwon, B.-H., Lee, M., & Lee, S. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3434983 · Cited by 7

Weighted informative inverse active class selection for motor imagery brain computer interface

Hossain, I., Khosravi, A., & Nahavandi, S. (2017) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2017.7946613 · Cited by 7

Analyzing Attention Deviation During Collaterally Proceeding Cognitive Tasks

Gogna, Y., Singla, R. K., & Tiwari, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5856-6_48 · Cited by 7

Frontiers of Immersive Gaming Technology: A Survey of Novel Game Interaction Design and Serious Games for Cognition

Stahlke, S., Bellyk, J. D., Meier, O. R., Mirza-Babaei, P., & Kapralos, B. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-59608-8_28 · Cited by 7

Neural indicators of the depth of cognitive processing for user-adaptive neurotechnological applications

Nicolae, I.-E., Acqualagna, L., & Blankertz, B. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318651 · Cited by 7

Calibration Time Reduction Using Subjective Features Selection Based Transfer Learning For Multiclass BCI

Hossain, I., Khosravi, A., Hettiarachchi, I. T., & Nahavandi, S. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00093 · Cited by 7

Efficient human-machine control with asymmetric marginal reliability input devices

Williamson, J. H., Quek, M., Popescu, I., Ramsay, A., & Murray‐Smith, R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233603 · Cited by 6

Sensorimotor brain–computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Kapralov, N., Idaji, M. J., Stephani, T., Studenova, A. A., Vidaurre, C., Ros, T., Villringer, A., & Nikulin, V. V. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad7a24 · Cited by 6

Mean shrinkage improves the classification of ERP signals by exploiting additional label information

Höhne, J., Blankertz, B., Müller, K., & Bartz, D. (2014) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2014.6858523 · Cited by 6

Real-time brain computer interface using imaginary movements

El-Madani, A., Sørensen, H. B. D., Kjær, T. W., Thomsen, C. E., & Puthusserypady, S. K. (2015) · EPJ Nonlinear Biomedical Physics · doi:10.1140/epjnbp/s40366-015-0024-2 · Cited by 6

Developing a Symbiotic System for Scientific Information Seeking: The MindSee Project

Gamberini, L., Spagnolli, A., Blankertz, B., Samuel Kaski, A., Freeman, J., Acqualagna, L., Barral, O., Bellio, M., Chech, L., Eugster, M. J. A., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24917-9_7 · Cited by 6

Open access repository for hybrid EEG-NIRS data

Shin, J., von Lühmann, A., Blankertz, B., Kim, D.-W., Mehnert, J., Jeong, J., Hwang, H., & Müller, K. (2018) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2018.8311523 · Cited by 6

Advances in high-dimensional covariance matrix estimation

Bartz, D. (2016) · DepositOnce · doi:10.14279/depositonce-5038 · Cited by 6

Ethical Reflections on Brain–Computer Interfaces

Bocquelet, F., Piret, G., Aumonier, N., & Yvert, B. (2016) · Wiley eBooks · doi:10.1002/9781119332428.ch15 · Cited by 7

Neuroergonomics on the go. A preview of the potential of mobile EEG for work-place evaluation and designPreprint

Wascher, E., Reiser, J. E., Rinkenauer, G., Larra, M. F., Dreger, F. A., Schneider, D. A., Karthaus, M., Getzmann, S., Gutberlet, M., & Arnau, S. (2020) · doi:10.31219/osf.io/vkf79 · Cited by 6

Functional connectivity guided deep neural network for decoding high-level visual imagery

Kwon, B.-H., Lee, M., & Lee, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128734 · Cited by 5

Decoding Brain States during Auditory Perception by Supervising Unsupervised Learning

Porbadnigk, A. K., Görnitz, N., Kloft, M., & Müller, K. (2013) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2013.7.2.112 · Cited by 5

Mutual Information-Based Electrode Selection Extended With Prior Knowledge For Use in Brain-Computer Interfacing

Moermans, R., Wittevrongel, B., & Hulle, M. M. V. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621501 · Cited by 5

Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals

Lee, D.-H., Kim, S.-J., & Choi, Y.-W. (2023) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci57258.2023.10078681 · Cited by 5

Development of Parkour Game System Using EEG Control

Wang, P., Yang, Y., & Li, J. (2018) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c.2018.00072 · Cited by 5

Neural–Computer Interfaces: Theory, Practice, Perspectives

Dubynin, I. A., Zemlyanskov, M. S., Shalayeva, I., Gorskii, O., Grinevich, V. P., & Евгеньевич, М. П. (2025) · Applied Sciences · doi:10.3390/app15168900 · Cited by 4

Brain-Computer Interface as Networking Entity in Body Area Networks

López-Gordo, M. A., & Valle, F. P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22572-2_20 · Cited by 4

Personal identification number application using brain-computer interface

Wilaiprasitporn, T., & Yagi, T. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146573 · Cited by 4

Binary classification with pFDR‐pFNR losses

Dickhaus, T., Blankertz, B., & Meinecke, F. C. (2013) · Biometrical Journal · doi:10.1002/bimj.201200054 · Cited by 4

The effect of stimulus type and distance on neural control of a smart TV

Kim, J. J., Hwang, T., Kim, M., Oh, E., Hwangbo, M., Kim, M., & Kim, S.-P. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6696190 · Cited by 4

The Power of Thoughts: A Qualitative Interview Study with Healthy Users of Brain-Computer Interfaces

Schmid, J. R., & Jox, R. J. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-64590-8_9 · Cited by 4

Unsupervised learning in a BCI chess application using label proportions and expectation-maximization

Hübner, D., Schall, A., & Tangermann, M. (2020) · Brain-Computer Interfaces · doi:10.1080/2326263x.2020.1741072 · Cited by 4

A Fast SSVEP-Based Brain-Computer Interface

Jorajuría, T., Gómez, M., & Vidaurre, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61705-9_5 · Cited by 4

Predicting Motor Intentions with Closed-Loop Brain-Computer Interfaces

Schultze-Kraft, M., Neumann, M., Lundfall, M., Wagner, P., Birman, D., Haynes, J., & Blankertz, B. (2017) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-64373-1_8 · Cited by 4

Can We Distinguish Pragmatic from Hedonic User Experience Qualities with Implicit Measures?

Pollmann, K., Sinram, V., Fronemann, N., & Vukelić, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91797-9_37 · Cited by 4

Decoding of walking Intention under Lower limb exoskeleton Environment using MRCP Feature

Jeong, J.-H., Kwak, N.-S., Lee, M.-H., & Lee, S.-W. (2017) · Graz Brain-Computer Interface Conference · doi:10.3217/978-3-85125-533-1-39 · Cited by 4

Predicting intentional and unintentional task unrelated thought with EEGPreprint

Martel, A., Arvaneh, M., Robertson, I. H., & Dockree, P. M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/764803 · Cited by 5

Modeling temporal dependency of brain responses to rapidly presented stimuli in ERP based BCIs

Fernàndez-Cañellas, D. (2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 4

Noninvasive Brain-Computer Interfaces

Allison, B. Z., & Krusienski, D. J. (2014) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_707-1 · Cited by 4

EFRM: A Multimodal EEG–fNIRS Representation-learning Model for few-shot brain-signal classification

Jung, E., & An, J. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111292 · Cited by 5

Decoding Fatigue Levels of Pilots using EEG Signals with Hybrid Deep Neural Networks

Lee, D.-H., Kim, S., & Kim, S. (2025) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci65088.2025.10931298 · Cited by 3

Decoding 3D Representation of Visual Imagery EEG using Attention-based Dual-Stream Convolutional Neural Network

Ahn, H.-J., & Lee, D.-H. (2022) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci53720.2022.9734836 · Cited by 3

Power spectrum analysis of EEG for watching 2D & 3D videos and resting state

Manshouri, N., Maleki, M., & Kayıkçıoğlu, T. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404394 · Cited by 3

Automatic stimuli classification from ERP data for augmented communication via Brain-Computer Interfaces

Leoni, J., Tanelli, M., Strada, S. C., Jiang, K., Brusa, A., & Proverbio, A. M. (2020) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms49158.2020.9209393 · Cited by 3

Hearables: In-Ear Multimodal Brain Computer Interfacing

Yarici, M. C., Davies, H. J., Nakamura, T., Williams, I., & Mandic, D. P. (2021) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-030-60460-8_7 · Cited by 3

Brain-Computer Interfaces: Current and Future Investigations in the Philosophy and Politics of Neurotechnology

Wolkenstein, A., & Friedrich, O. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-64590-8_6 · Cited by 3

A robust Gauss-Newton algorithm for analyzing Steady-State Visual Evoked Potentials

Riyahi, P., & Eskandarian, A. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6696185 · Cited by 3

Influence of Workload on Auditory Evoked Potentials in a Single-stimulus Paradigm

Roy, R. N., Breust, A., Bonnet, S., Porcherot, J., Charbonnier, S., Godin, C., & Campagne, A. (2015) · International Conference on Physiological Computing Systems (PhyCS) · doi:10.5220/0005235701040111 · Cited by 3

Advancement in the EEG-Based Chinese Spelling Systems

Shi, M., Zhou, C., Jiang, M., Hong, Q., Chao, F., Xie, J., Ren, W., Zhou, D., Yang, T., & Liu, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-43518-3_11 · Cited by 3

Toward a Secure Platform for Brain-Connected Devices—Issues and Current Solutions

Greenhorn, S. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.3005967 · Cited by 3

Decoding deceipt: EEG signatures of lying behavior under spontaneous versus instructed lying and truth-telling in a two-player gamePreprint

Chen, Y., Fazli, S., & Wallraven, C. (2023) · Research Square · doi:10.21203/rs.3.rs-2521275/v1 · Cited by 3

SMART-TeleLoad: A new graphic user interface to generate affective loads for teleoperation

Jo, W., Cha, G.-E., Foti, D., & Min, B. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101757 · Cited by 3

CMD$^{3}$: Cross-Modal Decoupled Deformable Distillation for EEG-fNIRS Fusion

Fan, T., Tian, F., Wang, S., Zhang, H., Luo, G., Zhu, L., Liu, J., Jiang, L., Cai, R., Dong, Q., et al. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3670503 · Cited by 2

Two Player Online Brain-Controlled Chess

Hübner, D., Schall, A., & Tangermann, M. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856965 · Cited by 2

Decoding EEG–based Workload Levels Using Spatio–temporal Features Under Flight Environment

Lee, D.-H., Kim, S.-J., & Kim, S. (2024) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci60775.2024.10480492 · Cited by 2

A Technical Exploration of Strategies for Augmented Monitoring and Decision Support in Information Warfare

Plaza, F. M., Román, I. H. S., & Monge, M. A. S. (2024) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3664476.3670922 · Cited by 2

Motor Imagery and Mental Arithmetic Classification Based on Transformer Deep Learning Network

Ye, Y., Tong, J., Yang, S., Chang, Y., & Du, S. (2024) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma61710.2024.10632904 · Cited by 2

A review on brain–computer interface and its applications

Bhonsle, D., Sheikh, R. U., Kshatri, S. S., Shukla, R., Agrawal, A. K., & Rathore, S. S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-21870-5.00009-1 · Cited by 2

The Future of Mental Health Care Toward an Integrative Paradigm

Lake, J. (2012) · InTech eBooks · doi:10.5772/30534 · Cited by 2

Feature Extraction from EEG Signals: A deep learning perspective

Mohammad, A., Siddiqui, F., & Alam, M. A. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377108 · Cited by 2

Motor Imagery EEG Signals Analysis Based on Bayesian Network with Gaussian Distribution

He, L., & Liu, B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09330-7_29 · Cited by 2

Decoding cognitive brain states

Porbadnigk, A. K., Treder, M. S., Fazli, S., Tangermann, M., Vidaurre, C., Haufe, S., Curio, G., Blankertz, B., & Miiller, K. R. (2013) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2013.6506613 · Cited by 2

Future Directions for Brain-Machine Interfacing Technology

Choi, K., & Min, B. (2015) · Trends in augmentation of human performance · doi:10.1007/978-94-017-7239-6_1 · Cited by 2

Moving Brain-Controlled Devices Outside the Lab: Principles and Applications

Leeb, R., Chavarriaga, R., Perdikis, S., Iturrate, I., & Millán, J. D. R. (2015) · Trends in augmentation of human performance · doi:10.1007/978-94-017-7239-6_6 · 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

Brain-Computer Interfaces: Agency and the Transition from Ethics to Politics

Wolkenstein, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58071-5_9 · Cited by 2

Creation of a high resolution EEG based Brain Computer Interface for classifying motor imagery of daily life activities

Chacko, S. G., Tayade, P., Kaur, S., & Sharma, R. (2019) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2019.8737258 · Cited by 2

A Wearable Device for Brain–Machine Interaction with Augmented Reality Head-Mounted Display

Salvaro, M., Benatti, S., Kartsch, V., Guermandi, M., & Luca Benini, C. (2020) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-29897-5_29 · 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

BCIRecog: An Optimized BCI System for Imagined Speech Recognition

Kannadasan, K., Haresh, M. V., Reddy, A. R., & Begum, B. S. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10308091 · Cited by 2

MEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?

Jorajuría, T., Nikulin, V. V., Kapralov, N., Gómez, M., & Vidaurre, C. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3339612 · Cited by 2

Toward Brain-Based Interaction Between Humans and Technology

Vukelić, M., Pollmann, K., & Peißner, M. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811926-6.00017-8 · Cited by 2

Real-time detection of P300 brain events: brain-computer interfaces for EEG-based communication aids

Alkhater, R. (2012) · Tuwhera (Auckland University of Technology) · Cited by 2

Measuring vigilant attention : predictive power of EEG derived measures on reaction time, subjective state and task performance

Douwel, F. K. W. K. (2016) · Cited by 2

Visual Motion Imagery Classification with Deep Neural Network based on Functional ConnectivityPreprint

Kwon, B.-H., Jeong, J.-H., & Lee, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.02851 · Cited by 2

EEGNet-Based Fine-Tuning for Mental Work Load Detection Using MEG Data: A Study on Multiple Sclerosis

Mijani, A. M., Akbarian, F., Amiri, H. K., Nagels, G., Schependom, J. V., & Zhan, L. (2026) · AI in neuroscience. · doi:10.1177/2997979x261430238 · Cited by 1

Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness

du Bois, N., Korik, A., Hodge, S., Hudson, L., Elahi, A. S., Bigirimana, A., Dayan, N., Sanchez-Bornot, J. M., McCann, A., Yelden, K., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01574-x · Cited by 2

Review of Applications for Wireless Brain-Computer Interface Systems

Woo, S., Shin, Y., Lee, S., & Lee, H.-N. (2014) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-4666-6094-6.ch008 · Cited by 1

Neurophysiological Signals Processing

Borghini, G., Aricò, P., Flumeri, G. D., & Babiloni, F. (2017) · Biosystems & biorobotics · doi:10.1007/978-3-319-58598-7_6 · Cited by 1

Oculomotor activity parameters of the operator in the P300 brain–computer interface with variating stimulus situations

Basul, I. A. (2017) · Experimental Psychology (Russia) · doi:10.17759/exppsy.2017100109 · Cited by 1

Numérisation et comportements humains : l’exemple de la fatigue cognitive des opérateurs aériens

Salomone, M., Fabre, L., & Barbaroux, P. (2018) · Technologie et innovation · doi:10.21494/iste.op.2018.0251 · Cited by 1

A Survey of Brain Computer Interface Using Non-Invasive MethodsPreprint

Ghosh, R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.13151 · Cited by 2

Moving Brain-Controlled Devices Outside the Lab: Principles and Applications

Leeb, R., Chavarriaga, R., Perdikis, S., Iturrate, I., & Millán, J. D. R. (2015) · Trends in augmentation of human performance · doi:10.1007/978-94-017-7239-6_6 · Cited by 2

Applications of brain–computer interface

Perdikis, S., Riccio, A., Cordella, F., Barresi, G., Zollo, L., Pichiorri, F., & Daly, I. P. (2026) · Institute of Physics eBooks · doi:10.1088/978-0-7503-5914-6ch6

Trends and Applications of Brain Computer Interfaces

Veer, K., Yadav, D., & Yadav, S. (2020) · Current Signal Transduction Therapy · doi:10.2174/1574362415999201124224606 · Cited by 4

Why Use A BCI If You Are Healthy

Allison, B., Graimann, B., & Gräser, A. (2013)