Literature DB >> 10501572

EEG-based control of a hand grasp neuroprosthesis.

R T Lauer1, P H Peckham, K L Kilgore.   

Abstract

The feasibility of using the EEG signal to operate a hand grasp neuroprosthesis was investigated. Two able-bodied subjects and one neuroprosthesis user were trained to control the amplitude of the beta rhythm recorded over the frontal areas. After 6 months, all subjects exhibited a high level of control, being able to use this signal to move a cursor to targets on a computer screen with a high (>90%) accuracy rate. Control over the EEG signal was unaffected by upper extremity movement or electrical activation of the muscles, indicating that this signal would be adequate for neuroprosthetic use. To test this concept, the neuroprosthesis user operated his system with the cortical signal, and was able to effectively manipulate several objects.

Mesh:

Year:  1999        PMID: 10501572     DOI: 10.1097/00001756-199906030-00026

Source DB:  PubMed          Journal:  Neuroreport        ISSN: 0959-4965            Impact factor:   1.837


  18 in total

1.  Control of a hand grasp neuroprosthesis using an electroencephalogram-triggered switch: demonstration of improvements in performance using wavepacket analysis.

Authors:  J M Heasman; T R D Scott; L Kirkup; R Y Flynn; V A Vare; C R Gschwind
Journal:  Med Biol Eng Comput       Date:  2002-09       Impact factor: 2.602

Review 2.  Brain computer interfaces, a review.

Authors:  Luis Fernando Nicolas-Alonso; Jaime Gomez-Gil
Journal:  Sensors (Basel)       Date:  2012-01-31       Impact factor: 3.576

3.  Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training.

Authors:  Christian Enzinger; Stefan Ropele; Franz Fazekas; Marisa Loitfelder; Faton Gorani; Thomas Seifert; Gudrun Reiter; Christa Neuper; Gert Pfurtscheller; Gernot Müller-Putz
Journal:  Exp Brain Res       Date:  2008-07-01       Impact factor: 1.972

4.  Discrimination of left and right leg motor imagery for brain-computer interfaces.

Authors:  Peter Boord; Ashley Craig; Yvonne Tran; Hung Nguyen
Journal:  Med Biol Eng Comput       Date:  2010-02-09       Impact factor: 2.602

5.  Adaptive neuron-to-EMG decoder training for FES neuroprostheses.

Authors:  Christian Ethier; Daniel Acuna; Sara A Solla; Lee E Miller
Journal:  J Neural Eng       Date:  2016-06-01       Impact factor: 5.379

6.  Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors.

Authors:  David T Bundy; Mark Wronkiewicz; Mohit Sharma; Daniel W Moran; Maurizio Corbetta; Eric C Leuthardt
Journal:  J Neural Eng       Date:  2012-05-22       Impact factor: 5.379

Review 7.  Brain-controlled muscle stimulation for the restoration of motor function.

Authors:  Christian Ethier; Lee E Miller
Journal:  Neurobiol Dis       Date:  2014-10-28       Impact factor: 5.996

Review 8.  Spinal cord injury: present and future therapeutic devices and prostheses.

Authors:  Simon F Giszter
Journal:  Neurotherapeutics       Date:  2008-01       Impact factor: 7.620

Review 9.  Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations.

Authors:  Marc W Slutzky
Journal:  Neuroscientist       Date:  2018-05-17       Impact factor: 7.519

10.  Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas.

Authors:  Cynthia A Chestek; Vikash Gilja; Christine H Blabe; Brett L Foster; Krishna V Shenoy; Josef Parvizi; Jaimie M Henderson
Journal:  J Neural Eng       Date:  2013-01-31       Impact factor: 5.379

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