Доступ предоставлен для: Guest
Critical Reviews™ in Biomedical Engineering

Выходит 6 номеров в год

ISSN Печать: 0278-940X

ISSN Онлайн: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

Brain-Computer Interfaces for Neurorehabilitation

Том 41, Выпуск 3, 2013, pp. 269-279
DOI: 10.1615/CritRevBiomedEng.2014010697
Get accessGet access

Краткое описание

Brain-computer interfaces (BCIs) enable control of computers and other assistive devices, such as neuro-prostheses, which are used for communication, movement restoration, neuro-modulation, and muscle stimulation, by using only signals measured directly from the brain. A BCI creates a new output channel for the brain to a computer or a device. This requires retrieval of signals of interest from the brain, and its use for neuro-rehabilitation by means of interfacing the signals to a computerized device. Brain signals such as action potentials from single neurons or nerve fibers, extracellular local field potentials (LFPs), electrocorticograms, electroencephalogram and its components such as the event-related brain potentials, real-time functional magnetic resonance imaging, near-infrared spectroscopy, and magneto-encephalogram have been used. BCIs are envisaged to be useful for communication, control and self-regulation of brain function. BCIs employ neurofeedback to enable operant conditioning to allow the user to learn using it. Paralytic conditions arising from stroke or other diseases are being targeted for BCI application. Neurofeedback strategies ranging from sensory feedback to direct brain stimulation are being employed. Existing BCIs are limited in their throughput in terms of letters per minute or commands per minute, and need extensive training to use the BCI. Further, they can cause rapid fatigue due to use and have limited adaptability to changes in the patient's brain state. The challenge before BCI technology for neuro-rehabilitation today is to enable effective clinical use of BCIs with minimal effort to set up and operate.

ЦИТИРОВАНО В
  1. Mattei Tobias A, How graphene is expected to impact neurotherapeutics in the near future, Expert Review of Neurotherapeutics, 14, 8, 2014. Crossref

  2. Shurkhay V. A., Aleksandrova E. V., Potapov A. A., Goryainov S. A., The current state of the brain—computer interface problem, Voprosy neirokhirurgii imeni N.N. Burdenko, 79, 1, 2015. Crossref

  3. Monge-Pereira Esther, Ibañez-Pereda Jaime, Alguacil-Diego Isabel M., Serrano Jose I., Spottorno-Rubio María P., Molina-Rueda Francisco, Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review, PM&R, 9, 9, 2017. Crossref

  4. Angulo-Sherman I. N., Costa-García A., Monge-Pereira E., Salazar-Varas R., Zerafa R., BCI Applied to Neurorehabilitation, in Emerging Therapies in Neurorehabilitation II, 10, 2016. Crossref

  5. Steinert Steffen, Bublitz Christoph, Jox Ralf, Friedrich Orsolya, Doing Things with Thoughts: Brain-Computer Interfaces and Disembodied Agency, Philosophy & Technology, 32, 3, 2019. Crossref

  6. Koganemaru Satoko, Mikami Yusuke, Maezawa Hitoshi, Ikeda Satoshi, Ikoma Katsunori, Mima Tatsuya, Neurofeedback Control of the Human GABAergic System Using Non-invasive Brain Stimulation, Neuroscience, 380, 2018. Crossref

  7. Bansal Dipali, Mahajan Rashima, EEG-Based Brain-Computer Interfacing (BCI), in EEG-Based Brain-Computer Interfaces, 2019. Crossref

  8. Singh Fiza, Shu I-Wei, Granholm Eric, Pineda Jaime A, Revisiting the Potential of EEG Neurofeedback for Patients With Schizophrenia, Schizophrenia Bulletin, 46, 4, 2020. Crossref

  9. Camargo-Vargas Daniela, Callejas-Cuervo Mauro, Mazzoleni Stefano, Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review, Sensors, 21, 13, 2021. Crossref

  10. Valenzuela Felipe, Rana Mohit, Sitaram Ranganatha, Uribe Sergio, Eblen-Zajjur Antonio, Non-Invasive Functional Evaluation of the Human Spinal Cord by Assessing the Peri-Spinal Neurovascular Network With Near Infrared Spectroscopy, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 2021. Crossref

  11. Dey Rajiv, Sahu Pankaj, Intelligent Systems in Healthcare, in Medical Imaging and Health Informatics, 2022. Crossref

  12. Le Franc Salomé, Herrera Altamira Gabriela, Guillen Maud, Butet Simon, Fleck Stéphanie, Lécuyer Anatole, Bougrain Laurent, Bonan Isabelle, Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives, Frontiers in Human Neuroscience, 16, 2022. Crossref

Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции Цены и условия подписки Begell House Контакты Language English 中文 Русский Português German French Spain