Literature DB >> 8973969

EEG-based communication: prospects and problems.

T M Vaughan1, J R Wolpaw, E Donchin.   

Abstract

Current rehabilitation engineering combines new prosthetic methods with recent developments in personal computers to provide alternative communication and control channels to individuals with motor impairments. Despite these advances, all commercially available systems still require some measure of voluntary motor control. Thus, these systems are not useful for individuals who are totally paralyzed. Electroencephalographic (EEG) activity may provide the basis for a system that would completely bypass normal motor output. EEG-based communication technology might provide assistive devices for individuals who have little or no reliable motor function. This paper reviews the prospects for and problems of EEG-based communication. It summarizes current approaches to development of this new technology, describes the major problems that must be resolved, and focuses on issues critical for its use by those with severe motor disabilities.

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Year:  1996        PMID: 8973969     DOI: 10.1109/86.547945

Source DB:  PubMed          Journal:  IEEE Trans Rehabil Eng        ISSN: 1063-6528


  7 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

2.  Model analyses of visual biofeedback training for EEG-based brain-computer interface.

Authors:  Chih-Wei Chen; Ming-Shaung Ju; Yun-Nien Sun; Chou-Ching K Lin
Journal:  J Comput Neurosci       Date:  2009-04-09       Impact factor: 1.621

3.  ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance.

Authors:  Surjo R Soekadar; Matthias Witkowski; Jürgen Mellinger; Ander Ramos; Niels Birbaumer; Leonardo G Cohen
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-10       Impact factor: 3.802

4.  Learning algorithms for human-machine interfaces.

Authors:  Zachary Danziger; Alon Fishbach; Ferdinando A Mussa-Ivaldi
Journal:  IEEE Trans Biomed Eng       Date:  2009-02-06       Impact factor: 4.538

5.  EEG classification of different imaginary movements within the same limb.

Authors:  Xinyi Yong; Carlo Menon
Journal:  PLoS One       Date:  2015-04-01       Impact factor: 3.240

6.  Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine.

Authors:  Sathees Kumar Nataraj; M P Paulraj; Sazali Bin Yaacob; Abdul Hamid Bin Adom
Journal:  J Med Signals Sens       Date:  2020-11-11

7.  Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers.

Authors:  Stefano Tortora; Luca Tonin; Carmelo Chisari; Silvestro Micera; Emanuele Menegatti; Fiorenzo Artoni
Journal:  Front Neurorobot       Date:  2020-11-17       Impact factor: 2.650

  7 in total

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