Brain–computer interface
Related papers: 20
About
A brain–computer interface (BCI) is a system that establishes a direct communication pathway between the brain and external devices by recording, interpreting, and translating neural signals into actionable commands. These signals can be captured invasively through implanted electrode arrays placed within or on the cortex, or noninvasively via electroencephalography (EEG) recorded from the scalp. In robotics and AI, BCIs enable users to control robotic arms, wheelchairs, prosthetics, and mobile robots using thought alone, bypassing damaged motor pathways entirely. Machine learning algorithms decode neural patterns—such as motor imagery signals—and map them to device commands in real time. BCIs matter profoundly because they offer restored mobility and communication to people with paralysis, tetraplegia, or stroke-related motor impairments. Beyond assistive applications, they support neurorehabilitation by promoting activity-dependent brain plasticity, potentially accelerating motor recovery. As electrode technology and decoding algorithms advance, BCIs are moving from laboratory demonstrations toward practical, high-performance clinical tools that could transform the lives of millions with neurological disabilities.
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Top Cited Papers
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Leigh R. Hochberg, Daniel Bacher, Beata Jarosiewicz, Nicolas Y. Masse, John D. Simeral, Jörn Vogel, Sami Haddadin, Jie Liu, Sydney S. Cash, Patrick van der Smagt, John P. Donoghue
Citations: 2730 • 2012
Motor recovery after stroke: a systematic review
Peter Langhorne, Fiona Coupar, Alex Pollock
Citations: 2294 • 2009
Brain–machine interfaces: past, present and future
Mikhail Lebedev, Miguel A. L. Nicolelis
Citations: 1854 • 2006
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Jonathan R. Wolpaw, Dennis J. McFarland
Citations: 1516 • 2004
An Integrated Brain-Machine Interface Platform With Thousands of Channels
Elon Musk
Citations: 1055 • 2019
Principles of Animal Communication
Mark E. Laidre
Citations: 987 • 2012
Brain–computer interfaces for communication and rehabilitation
Ujwal Chaudhary, Niels Birbaumer, Ander Ramos‐Murguialday
Citations: 904 • 2016
Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG
José del R. Millán, F. Renkens, J. Mouriño, Wulfram Gerstner
Citations: 739 • 2004
Brain-Computer Interfaces in Medicine
Jerry J. Shih, Dean J. Krusienski, Jonathan R. Wolpaw
Citations: 711 • 2012
A high-performance brain–computer interface
Gopal Santhanam, Stephen I. Ryu, Byron M. Yu, Afsheen Afshar, Krishna V. Shenoy
Citations: 707 • 2006
A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots
Ferran Galán, Marnix Nuttin, Eileen Lew, Pierre W. Ferrez, Gerolf Vanacker, Johan Philips, José del R. Millán
Citations: 666 • 2008
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
Mikhail Lebedev, Miguel A. L. Nicolelis
Citations: 644 • 2017
Active tactile exploration using a brain–machine–brain interface
Joseph E. O’Doherty, Mikhail Lebedev, Peter J. Ifft, Katie Zhuang, Solaiman Shokur, Hannes Bleuler, Miguel A. L. Nicolelis
Citations: 634 • 2011
Direct control of paralysed muscles by cortical neurons
Chet T. Moritz, Steve I. Perlmutter, Eberhard E. Fetz
Citations: 606 • 2008
Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface
K. R. LaFleur, Kaitlin Cassady, Alexander Doud, Kaleb Shades, Eitan Rogin, Bin He
Citations: 586 • 2013
A high-performance neural prosthesis enabled by control algorithm design
Vikash Gilja, Paul Nuyujukian, Cindy A Chestek, John P. Cunningham, Byron M. Yu, Joline M. Fan, Mark M. Churchland, Matthew T. Kaufman, Jonathan C. Kao, Stephen I. Ryu, Krishna V. Shenoy
Citations: 586 • 2012
Rehabilitation of gait after stroke: a review towards a top-down approach
Juan Manuel Belda Lois, Silvia Mena-del Horno, Ignacio Bermejo-Bosch, Juan C. Moreno, José L. Pons, Dario Farina, Marco Iosa, Marco Molinari, Federica Tamburella, Ander Ramos, Andrea Carìa, Teodoro Solis‐Escalante, Clemens Brunner, Massimiliano Rea
Citations: 580 • 2011
A brain-computer interface that evokes tactile sensations improves robotic arm control
Sharlene N. Flesher, John E. Downey, Jeffrey M. Weiss, Christopher Hughes, Angelica J. Herrera, Elizabeth C. Tyler‐Kabara, Michael L. Boninger, Jennifer L. Collinger, Robert A. Gaunt
Citations: 579 • 2021
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review
Hamdi Altaheri, Ghulam Muhammad, Mansour Alsulaiman, Syed Umar Amin, Ghadir Ali Altuwaijri, Wadood Abdul, Mohamed A. Bencherif, Mohammed Faisal
Citations: 558 • 2021
A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke
Kai Keng Ang, Karen Sui Geok Chua, Kok Soon Phua, Chuanchu Wang, Zheng Yang Chin, Christopher Wee Keong Kuah, Wilson Low, Cuntai Guan
Citations: 533 • 2014