Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

He, Bin; Baxter, Bryan; Edelman, Bradley J.; Cline, Christopher C.; Ye, Wenjing W.

doi:10.1109/jproc.2015.2407272

Cited by 233EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyMuscle activation and electromyography studies

Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

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He, Bin, Baxter, Bryan, Edelman, Bradley J., Cline, Christopher C., Ye, Wenjing W.·2015·Proceedings of the IEEE

DOI 10.1109/jproc.2015.2407272PMC PMC8323842

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Abstract

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

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Self-citation
Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm
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Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns
Wang, T., Deng, J., & He, B. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.06.022 · Cited by 203 · see citing works
Self-citation
Brain polarization enhances the formation and retention of motor memories.
Galea, J., & Celnik, P. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00184.2009 · Cited by 202 · see citing works
"Virtual keyboard" controlled by spontaneous EEG activity
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A Closed-Loop Brain–Computer Interface Triggering an Active Ankle–Foot Orthosis for Inducing Cortical Neural Plasticity
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Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery
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Brain—Computer Interface
Vallabhaneni, A., Wang, T., & He, B. (2007) · Springer eBooks · doi:10.1007/0-306-48610-5_3 · Cited by 182 · see citing works
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Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem
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A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP
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Vibrotactile Feedback for Brain-Computer Interface Operation
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Relationship between speed and EEG activity during imagined and executed hand movements
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Self-citation
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Self-citation
A translational platform for prototyping closed-loop neuromodulation systems
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Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis
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Self-citation
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Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving
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Modulation of mu rhythm desynchronization during motor imagery by transcranial direct current stimulation
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High-Definition Transcranial Direct Current Stimulation Induces Both Acute and Persistent Changes in Broadband Cortical Synchronization: A Simultaneous tDCS–EEG Study
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Predicting the behavioral impact of transcranial direct current stimulation: issues and limitations
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EEG-Based Classification of Fast and Slow Hand Movements Using Wavelet-CSP Algorithm
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Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain
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An analysis of performance evaluation for motor-imagery based BCI
Thomas, E. M., Dyson, M., & Clerc, M. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/031001 · Cited by 116 · see citing works
High-resolution EEG techniques for brain-computer interface applications
Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Astolfi, L., De Vico Fallani, F., Tocci, A., Bianchi, L., Marciani, M. G., Gao, S., et al. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.06.031 · Cited by 115 · see citing works
Brain-computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users
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Biased feedback in brain-computer interfaces
Barbero, Á., & Grosse‐Wentrup, M. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-34 · Cited by 111 · see citing works
Bimodal BCI Using Simultaneously NIRS and EEG
Tomita, Y., Vialatte, F., Dreyfus, G., Mitsukura, Y., Bakardjian, H., & Cichocki, A. S. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300492 · Cited by 110 · see citing works
Brain–computer interface: Changes in performance using virtual reality techniques
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Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI
Yao, L., Meng, J., Zhang, D., Sheng, X., & Zhu, X. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2287245 · Cited by 98 · see citing works
Synchronous EEG brain-actuated wheelchair with automated navigation
Iturrate, I., Antelis, J. M., & Mínguez, J. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152580 · Cited by 95 · see citing works
Cue-induced beta rebound during withholding of overt and covert foot movement
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In vivo assessment of human brain oscillations during application of transcranial electric currents
Soekadar, S. R., Witkowski, M., Cossio, E. G., Birbaumer, N., Robinson, S. E., & Cohen, L. G. (2013) · Nature Communications · doi:10.1038/ncomms3032 · Cited by 93 · see citing works
Classification of movement intention by spatially filtered electromagnetic inverse solutions
Congedo, M., Lotte, F., & Lécuyer, A. (2006) · Physics in Medicine and Biology · doi:10.1088/0031-9155/51/8/002 · Cited by 92 · see citing works
Thinking Penguin: Multimodal Brain–Computer Interface Control of a VR Game
Leeb, R., Lancelle, M., Kaiser, V., Fellner, D. W., & Pfurtscheller, G. (2013) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2013.2242072 · Cited by 91 · see citing works
Whatever Works: A Systematic User-Centered Training Protocol to Optimize Brain-Computer Interfacing Individually
Friedrich, E. V. C., Neuper, C., & Scherer, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076214 · Cited by 88 · see citing works
Fast attainment of computer cursor control with noninvasively acquired brain signals
Bradberry, T. J., Gentili, R. J., & Contreras-Vidal, J. L. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/3/036010 · Cited by 86 · see citing works
Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller
Perdikis, S., Leeb, R., Williamson, J., Ramsay, A., Tavella, M., Desideri, L., Hoogerwerf, E.-J., Al-Khodairy, A., Murray-Smith, R., & Millán, J. D. R. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/036003 · Cited by 85 · see citing works
Brain–Computer Interfaces
He, B., Gao, S., Yuan, H., & Wolpaw, J. R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5227-0_2 · Cited by 84 · see citing works
Self-citation
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 82 · see citing works
Enhanced EEG alpha time-domain phase synchrony during Transcendental Meditation: Implications for cortical integration theory
Hebert, R., Lehmann, D., Tan, G., Travis, F., & Arenander, A. (2005) · Signal Processing · doi:10.1016/j.sigpro.2005.07.009 · Cited by 82 · see citing works
Effect of mindfulness meditation on brain–computer interface performance
Tan, L. F., Dienes, Z. P., Jansari, A. S., & Goh, S.-Y. (2013) · Consciousness and Cognition · doi:10.1016/j.concog.2013.10.010 · Cited by 78 · see citing works
Enhancing Motor Skill Learning with Transcranial Direct Current Stimulation – A Concise Review with Applications to Stroke
Madhavan, S., & Shah, B. (2012) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2012.00066 · Cited by 77 · see citing works
SSVEP-Based Brain–Computer Interfaces Using FSK-Modulated Visual Stimuli
Kimura, Y., Tanaka, T., Higashi, H., & Morikawa, N. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2265260 · Cited by 75 · see citing works
Single-Trial EEG Source Reconstruction for Brain–Computer Interface
Noirhomme, Q., Kitney, R. I., & Macq, B. M. M. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.913986 · Cited by 75 · see citing works
Modulation of event-related desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke
Kasashima, Y., Fujiwara, T., Matsushika, Y., Tsuji, T., Hase, K., Ushiyama, J., Ushiba, J., & Liu, M. (2012) · Experimental Brain Research · doi:10.1007/s00221-012-3166-9 · Cited by 73 · see citing works
Navigating Virtual Reality by Thought: What Is It Like?
Friedman, D. A., Leeb, R., Guger, C., Steed, A. J., Pfurtscheller, G., & Slater, M. (2007) · PRESENCE Virtual and Augmented Reality · doi:10.1162/pres.16.1.100 · Cited by 73 · see citing works
Grand Challenges in Mapping the Human Brain: NSF Workshop Report
He, B., Coleman, T. P., Genin, G. M., Glover, G. H., Hu, X. P., Johnson, N., Liu, T., Makeig, S., Sajda, P., & Ye, K. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2283970 · Cited by 70 · see citing works
Self-citation
P300-Based Brain-Neuronal Computer Interaction for Spelling Applications
Postelnicu, C.-C., & Talabă, D. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2228645 · Cited by 67 · see citing works
Self-initiation of EEG-based brain–computer communication using the heart rate response
Scherer, R., Muller-Putz, G. R., & Pfurtscheller, G. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/4/l01 · Cited by 66 · see citing works
Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback
Boe, S. G., Gionfriddo, A., Kraeutner, S. N., Tremblay, A., Little, G. E., & Bardouille, T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.066 · Cited by 64 · see citing works
Self-paced brain–computer interface control of ambulation in a virtual reality environment
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Exploring Large Virtual Environments by Thoughts Using a Brain–Computer Interface Based on Motor Imagery and High-Level Commands
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Long-term evaluation of a 4-class imagery-based brain–computer interface
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Grand Challenges in Interfacing Engineering With Life Sciences and Medicine
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Self-citation
Observation-based learning for brain–machine interfaces
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rtMEG: A Real-Time Software Interface for Magnetoencephalography
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Improving the performance of brain-computer interface through meditation practicing
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An Online Semi-supervised Brain–Computer Interface
Gu, Z., Yu, Z., Shen, Z., & Li, Y. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2261994 · Cited by 53 · see citing works
Je pense donc je fais: transcranial direct current stimulation modulates brain oscillations associated with motor imagery and movement observation
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Learned EEG-based brain self-regulation of motor-related oscillations during application of transcranial electric brain stimulation: feasibility and limitations
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The impact of mind-body awareness training on the early learning of a brain-computer interface
Cassady, K. E., You, A. K., Doud, A., & He, B. (2014) · TECHNOLOGY · doi:10.1142/s233954781450023x · Cited by 46 · see citing works
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A second-order finite element algorithm for solving the three-dimensional EEG forward problem
Zhang, Y. C., Zhu, S. A., & He, B. (2004) · Physics in Medicine and Biology · doi:10.1088/0031-9155/49/13/014 · Cited by 46 · see citing works
Self-citation
A Unified Probabilistic Approach to Improve Spelling in an Event-Related Potential-Based Brain–Computer Interface
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Non-invasive estimation of local field potentials for neuroprosthesis control
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Targeted reinforcement of neural oscillatory activity with real-time neuroimaging feedback
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Navigating in Virtual Worlds Using a Self-Paced SSVEP-Based Brain–Computer Interface with Integrated Stimulation and Real-Time Feedback
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Brain–machine interface control of a manipulator using small-world neural network and shared control strategy
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A hybrid BCI for enhanced control of a telepresence robot
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Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery
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Electrophysiological Mapping and Neuroimaging
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Self-citation
Equivalent dipole estimation of spontaneous EEG alpha activity: two-moving dipole approach
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Self-citation
Motor‐related cortical dynamics to intact movements in tetraplegics as revealed by high‐resolution EEG
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An EEG-based real-time cortical rhythmic activity monitoring system
Im, C., Hwang, H., Che, H., & Lee, S. H. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/9/011 · Cited by 20 · see citing works
Neuroelectrical source imaging of mu rhythm control for BCI applications
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Thought-based interaction with the physical world
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Discriminating hand gesture motor imagery tasks using cortical current density estimation
Edelman, B. J., Baxter, B., & He, B. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943840 · Cited by 12 · see citing works
Self-citation
SOURCE RECONSTRUCTION AND SYNCHRONY MEASUREMENTS FOR REVEALING FUNCTIONAL BRAIN NETWORKS AND CLASSIFYING MENTAL STATES
Laurent, F., Besserve, M., Garnero, L., Philippe, M., Florence, G., & Martinerie, J. M. (2010) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127410026770 · Cited by 2 · see citing works

Flagged first, then by citations

5 of the 177 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

[3]Vallabhaneni A, Wang T, and He B, “ Brain-Computer Interface,” In Neural Engineering, ed. He B, Kruwer Academic Publishers, 2005, pp. 85– 121.
[7]He B, Gao S, Yuan H, and Wolpaw JR, “ Brain-Computer Interface,” In Neural Engineering, 2nd Ed., ed. He B, Boston, MA: Springer US, 2013, pp. 87– 151.
[88]He B and Ding L, “ Electrophysiological Neuroimaging,” In Neural Engineering, 2nd Ed., ed. He B, Boston, MA: Springer US, 2013, pp. 499– 543.

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Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devicesPreprint
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Authors: He, B., Baxter, B., Edelman, B. J., Cline, C. C., & Ye, W. W.
Affiliations: University of Minnesota (US)
Corresponding author: He, Bin
ORCID: He, Bin
Baxter, Bryan
Edelman, Bradley J.
Cline, Christopher C.
Venue: Proceedings of the IEEE
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2015-06
Volume: 103
Issue: 6
Pages: 907-925
ISSN: 0018-9219 (ISSN-L)
1558-2256
DOI: 10.1109/jproc.2015.2407272
PMC: PMC8323842
OpenAlex: W2147854680
Cited by: 233
References: 177
Citations by year: 2026: 12 · 2025: 18 · 2024: 28 · 2023: 24 · 2022: 15 · 2021: 21 · 2020: 17 · 2019: 20 · 2018: 33 · 2017: 27 · 2016: 14 · 2015: 4
FWCI: 15.63
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: Proceedings of the IEEE (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation (CBET-1264782, DGE-1069104)
National Institute of Health (EB006433, EY023101)
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: non-invasive brain-computer interface, sensorimotor rhythm, EEG, cortical source localization, non-invasive neuromodulation, motor control, communication aids

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Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

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