Literature DB >> 12403992

Connecting cortex to machines: recent advances in brain interfaces.

John P Donoghue1.   

Abstract

Recent technological and scientific advances have generated wide interest in the possibility of creating a brain-machine interface (BMI), particularly as a means to aid paralyzed humans in communication. Advances have been made in detecting neural signals and translating them into command signals that can control devices. We now have systems that use externally derived neural signals as a command source, and faster and potentially more flexible systems that directly use intracortical recording are being tested. Studies in behaving monkeys show that neural output from the motor cortex can be used to control computer cursors almost as effectively as a natural hand would carry out the task. Additional research findings explore the possibility of using computers to return behaviorally useful feedback information to the cortex. Although significant scientific and technological challenges remain, progress in creating useful human BMIs is accelerating.

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Year:  2002        PMID: 12403992     DOI: 10.1038/nn947

Source DB:  PubMed          Journal:  Nat Neurosci        ISSN: 1097-6256            Impact factor:   24.884


  118 in total

1.  Long term in vitro stability of fully integrated wireless neural interfaces based on Utah slant electrode array.

Authors:  Asha Sharma; Loren Rieth; Prashant Tathireddy; Reid Harrison; Florian Solzbacher
Journal:  Appl Phys Lett       Date:  2010-02-17       Impact factor: 3.791

Review 2.  Brain control and information transfer.

Authors:  Edward J Tehovnik; Lewis L Chen
Journal:  Exp Brain Res       Date:  2015-08-30       Impact factor: 1.972

3.  A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications.

Authors:  Lei Qin; Bin He
Journal:  J Neural Eng       Date:  2005-08-15       Impact factor: 5.379

4.  Identification of multiple-input systems with highly coupled inputs: application to EMG prediction from multiple intracortical electrodes.

Authors:  David T Westwick; Eric A Pohlmeyer; Sara A Solla; Lee E Miller; Eric J Perreault
Journal:  Neural Comput       Date:  2006-02       Impact factor: 2.026

Review 5.  Neural engineering to produce in vitro nerve constructs and neurointerface.

Authors:  Bryan J Pfister; Jason H Huang; Niranjan Kameswaran; Eric L Zager; Douglas H Smith
Journal:  Neurosurgery       Date:  2007-01       Impact factor: 4.654

6.  Improvement of spike train decoder under spike detection and classification errors using support vector machine.

Authors:  Kyung Hwan Kim; Sung Shin Kim; Sung June Kim
Journal:  Med Biol Eng Comput       Date:  2006-03       Impact factor: 2.602

7.  Conversion of functional synapses into silent synapses in the trigeminal brainstem after neonatal peripheral nerve transection.

Authors:  Fu-Sun Lo; Reha S Erzurumlu
Journal:  J Neurosci       Date:  2007-05-02       Impact factor: 6.167

8.  Detecting neural-state transitions using hidden Markov models for motor cortical prostheses.

Authors:  Caleb Kemere; Gopal Santhanam; Byron M Yu; Afsheen Afshar; Stephen I Ryu; Teresa H Meng; Krishna V Shenoy
Journal:  J Neurophysiol       Date:  2008-07-09       Impact factor: 2.714

9.  Encoding of speed and direction of movement in the human supplementary motor area.

Authors:  Ariel Tankus; Yehezkel Yeshurun; Tamar Flash; Itzhak Fried
Journal:  J Neurosurg       Date:  2009-06       Impact factor: 5.115

Review 10.  Decoding Cognitive Processes from Neural Ensembles.

Authors:  Joni D Wallis
Journal:  Trends Cogn Sci       Date:  2018-09-29       Impact factor: 20.229

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