Literature DB >> 1707798

An EEG-based brain-computer interface for cursor control.

J R Wolpaw1, D J McFarland, G W Neat, C A Forneris.   

Abstract

This study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.

Entities:  

Mesh:

Year:  1991        PMID: 1707798     DOI: 10.1016/0013-4694(91)90040-b

Source DB:  PubMed          Journal:  Electroencephalogr Clin Neurophysiol        ISSN: 0013-4694


  130 in total

1.  Selecting relevant electrode positions for classification tasks based on the electro-encephalogram.

Authors:  T Müller; T Ball; R Kristeva-Feige; T Mergner; J Timmer
Journal:  Med Biol Eng Comput       Date:  2000-01       Impact factor: 2.602

2.  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 3.  Brain-computer interfaces in medicine.

Authors:  Jerry J Shih; Dean J Krusienski; Jonathan R Wolpaw
Journal:  Mayo Clin Proc       Date:  2012-02-10       Impact factor: 7.616

4.  Classification of multichannel EEG patterns using parallel hidden Markov models.

Authors:  Dror Lederman; Joseph Tabrikian
Journal:  Med Biol Eng Comput       Date:  2012-03-10       Impact factor: 2.602

5.  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

6.  Adaptive feature extraction for EEG signal classification.

Authors:  Shiliang Sun; Changshui Zhang
Journal:  Med Biol Eng Comput       Date:  2006-09-12       Impact factor: 2.602

7.  Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording.

Authors:  Christopher Black; Jakob Voigts; Uday Agrawal; Max Ladow; Juan Santoyo; Christopher Moore; Stephanie Jones
Journal:  J Neural Eng       Date:  2017-03-07       Impact factor: 5.379

8.  An enhanced time-frequency-spatial approach for motor imagery classification.

Authors:  Nobuyuki Yamawaki; Christopher Wilke; Zhongming Liu; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-06       Impact factor: 3.802

9.  Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

Authors:  Ou Bai; Peter Lin; Sherry Vorbach; Jiang Li; Steve Furlani; Mark Hallett
Journal:  Clin Neurophysiol       Date:  2007-10-29       Impact factor: 3.708

10.  The advantages of the surface Laplacian in brain-computer interface research.

Authors:  Dennis J McFarland
Journal:  Int J Psychophysiol       Date:  2014-08-01       Impact factor: 2.997

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.