Literature DB >> 12797728

A general framework for brain-computer interface design.

Steven G Mason1, Gary E Birch.   

Abstract

The Brain-Computer Interface (BCI) research community has acknowledged that researchers are experiencing difficulties when they try to compare the BCI techniques described in the literature. In response to this situation, the community has stressed the need for objective methods to compare BCI technologies. Suggested improvements have included the development and use of benchmark applications and standard data sets. However, as a young, multidisciplinary research field, the BCI community lacks a common vocabulary. As a result, this deficiency leads to poor intergroup communication, which hinders the development of the desired methods of comparison. One of the principle reasons for the lack of common vocabulary is the absence of a common functional model of a BCI System. This paper proposes a new functional model for BCI System design. The model supports many features that facilitate the comparison of BCI technologies with other BCI and non-BCI user interface technologies. From this model, taxonomy for BCI System design is developed. Together the model and taxonomy are considered a general framework for BCI System design. The representational power of the proposed framework was evaluated by applying it to a set of existing BCI technologies. The framework could effectively describe all of the BCI System designs tested.

Mesh:

Year:  2003        PMID: 12797728     DOI: 10.1109/TNSRE.2003.810426

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  18 in total

Review 1.  Brain computer interfaces, a review.

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

2.  Development of a Wearable Motor-Imagery-Based Brain-Computer Interface.

Authors:  Bor-Shing Lin; Jeng-Shyang Pan; Tso-Yao Chu; Bor-Shyh Lin
Journal:  J Med Syst       Date:  2016-01-09       Impact factor: 4.460

3.  Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata.

Authors:  Saugat Bhattacharyya; Abhronil Sengupta; Tathagatha Chakraborti; Amit Konar; D N Tibarewala
Journal:  Med Biol Eng Comput       Date:  2013-10-29       Impact factor: 2.602

4.  Performance measurement for brain-computer or brain-machine interfaces: a tutorial.

Authors:  David E Thompson; Lucia R Quitadamo; Luca Mainardi; Khalil Ur Rehman Laghari; Shangkai Gao; Pieter-Jan Kindermans; John D Simeral; Reza Fazel-Rezai; Matteo Matteucci; Tiago H Falk; Luigi Bianchi; Cynthia A Chestek; Jane E Huggins
Journal:  J Neural Eng       Date:  2014-05-19       Impact factor: 5.379

5.  Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

Authors:  Saugat Bhattacharyya; Amit Konar; D N Tibarewala
Journal:  Med Biol Eng Comput       Date:  2014-09-30       Impact factor: 2.602

6.  A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms.

Authors:  Mehrdad Fatourechi; Gary E Birch; Rabab K Ward
Journal:  J Comput Neurosci       Date:  2007-01-10       Impact factor: 1.621

7.  A multi-purpose brain-computer interface output device.

Authors:  David E Thompson; Jane E Huggins
Journal:  Clin EEG Neurosci       Date:  2011-10       Impact factor: 1.843

8.  The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study.

Authors:  Femke Nijboer; Niels Birbaumer; Andrea Kübler
Journal:  Front Neurosci       Date:  2010-07-21       Impact factor: 4.677

9.  Analysis of eyes open, eye closed EEG signals using second-order difference plot.

Authors:  Ranjit A Thuraisingham; Yvonne Tran; Peter Boord; Ashley Craig
Journal:  Med Biol Eng Comput       Date:  2007-10-10       Impact factor: 2.602

10.  A training platform for many-dimensional prosthetic devices using a virtual reality environment.

Authors:  David Putrino; Yan T Wong; Adam Weiss; Bijan Pesaran
Journal:  J Neurosci Methods       Date:  2014-04-13       Impact factor: 2.390

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.