Literature DB >> 28446119

Neurobionics and the brain-computer interface: current applications and future horizons.

Jeffrey V Rosenfeld1, Yan Tat Wong2.   

Abstract

The brain-computer interface (BCI) is an exciting advance in neuroscience and engineering. In a motor BCI, electrical recordings from the motor cortex of paralysed humans are decoded by a computer and used to drive robotic arms or to restore movement in a paralysed hand by stimulating the muscles in the forearm. Simultaneously integrating a BCI with the sensory cortex will further enhance dexterity and fine control. BCIs are also being developed to: provide ambulation for paraplegic patients through controlling robotic exoskeletons; restore vision in people with acquired blindness; detect and control epileptic seizures; and improve control of movement disorders and memory enhancement. High-fidelity connectivity with small groups of neurons requires microelectrode placement in the cerebral cortex. Electrodes placed on the cortical surface are less invasive but produce inferior fidelity. Scalp surface recording using electroencephalography is much less precise. BCI technology is still in an early phase of development and awaits further technical improvements and larger multicentre clinical trials before wider clinical application and impact on the care of people with disabilities. There are also many ethical challenges to explore as this technology evolves.

Entities:  

Mesh:

Year:  2017        PMID: 28446119     DOI: 10.5694/mja16.01011

Source DB:  PubMed          Journal:  Med J Aust        ISSN: 0025-729X            Impact factor:   7.738


  14 in total

Review 1.  Progress in Brain Computer Interface: Challenges and Opportunities.

Authors:  Simanto Saha; Khondaker A Mamun; Khawza Ahmed; Raqibul Mostafa; Ganesh R Naik; Sam Darvishi; Ahsan H Khandoker; Mathias Baumert
Journal:  Front Syst Neurosci       Date:  2021-02-25

2.  Feasibility of Nitrogen Doped Ultrananocrystalline Diamond Microelectrodes for Electrophysiological Recording From Neural Tissue.

Authors:  Yan T Wong; Arman Ahnood; Matias I Maturana; William Kentler; Kumaravelu Ganesan; David B Grayden; Hamish Meffin; Steven Prawer; Michael R Ibbotson; Anthony N Burkitt
Journal:  Front Bioeng Biotechnol       Date:  2018-06-22

3.  The Self-Face Paradigm Improves the Performance of the P300-Speller System.

Authors:  Zhaohua Lu; Qi Li; Ning Gao; Jingjing Yang
Journal:  Front Comput Neurosci       Date:  2020-01-15       Impact factor: 2.380

4.  Effectiveness and safety of brain-computer interface technology in the treatment of poststroke motor disorders: a protocol for systematic review and meta-analysis.

Authors:  Xiaolin Zhang; Di Cao; Junnan Liu; Qi Zhang; Mingjun Liu
Journal:  BMJ Open       Date:  2021-01-28       Impact factor: 2.692

5.  An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces.

Authors:  Sotirios Papadopoulos; James Bonaiuto; Jérémie Mattout
Journal:  Front Neurosci       Date:  2022-01-12       Impact factor: 4.677

Review 6.  Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.

Authors:  Roberto Portillo-Lara; Bogachan Tahirbegi; Christopher A R Chapman; Josef A Goding; Rylie A Green
Journal:  APL Bioeng       Date:  2021-07-20

7.  Effects of brain-computer interface training on upper limb function recovery in stroke patients: A protocol for systematic review and meta-analysis.

Authors:  Xiali Xue; Huan Tu; Zhongyi Deng; Ling Zhou; Ning Li; Xiaokun Wang
Journal:  Medicine (Baltimore)       Date:  2021-06-11       Impact factor: 1.817

Review 8.  Strategies and prospects of effective neural circuits reconstruction after spinal cord injury.

Authors:  Biao Yang; Feng Zhang; Feng Cheng; Liwei Ying; Chenggui Wang; Kesi Shi; Jingkai Wang; Kaishun Xia; Zhe Gong; Xianpeng Huang; Cao Yu; Fangcai Li; Chengzhen Liang; Qixin Chen
Journal:  Cell Death Dis       Date:  2020-06-08       Impact factor: 8.469

Review 9.  Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review.

Authors:  Kadir A Yildiz; Alexander Y Shin; Kenton R Kaufman
Journal:  J Neuroeng Rehabil       Date:  2020-03-10       Impact factor: 4.262

10.  Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task.

Authors:  Sue Peters; Shannon B Lim; Dennis R Louie; Chieh-Ling Yang; Janice J Eng
Journal:  J Neuroeng Rehabil       Date:  2020-08-10       Impact factor: 4.262

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