Literature DB >> 24579648

Brain-computer interfaces for neurorehabilitation.

Sujesh Sreedharan1, Ranganatha Sitaram2, Joseph S Paul3, C Kesavadas4.   

Abstract

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.

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Year:  2013        PMID: 24579648     DOI: 10.1615/critrevbiomedeng.2014010697

Source DB:  PubMed          Journal:  Crit Rev Biomed Eng        ISSN: 0278-940X


  3 in total

1.  Revisiting the Potential of EEG Neurofeedback for Patients With Schizophrenia.

Authors:  Fiza Singh; I-Wei Shu; Eric Granholm; Jaime A Pineda
Journal:  Schizophr Bull       Date:  2020-07-08       Impact factor: 9.306

Review 2.  Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives.

Authors:  Salomé Le Franc; Gabriela Herrera Altamira; Maud Guillen; Simon Butet; Stéphanie Fleck; Anatole Lécuyer; Laurent Bougrain; Isabelle Bonan
Journal:  Front Hum Neurosci       Date:  2022-07-14       Impact factor: 3.473

Review 3.  Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.

Authors:  Daniela Camargo-Vargas; Mauro Callejas-Cuervo; Stefano Mazzoleni
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

  3 in total

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