Literature DB >> 15218939

Continuous EEG classification during motor imagery--simulation of an asynchronous BCI.

George Townsend1, Bernhard Graimann, Gert Pfurtscheller.   

Abstract

Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.

Mesh:

Year:  2004        PMID: 15218939     DOI: 10.1109/TNSRE.2004.827220

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


  30 in total

1.  Automatic user customization for improving the performance of a self-paced brain interface system.

Authors:  Mehrdad Fatourechi; Ali Bashashati; Gary E Birch; Rabab K Ward
Journal:  Med Biol Eng Comput       Date:  2006-11-17       Impact factor: 2.602

2.  A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training.

Authors:  Chun Sing Louis Tsui; John Q Gan; Stephen J Roberts
Journal:  Med Biol Eng Comput       Date:  2009-02-19       Impact factor: 2.602

3.  Describing different brain computer interface systems through a unique model: a UML implementation.

Authors:  Lucia Rita Quitadamo; Maria Grazia Marciani; Gian Carlo Cardarilli; Luigi Bianchi
Journal:  Neuroinformatics       Date:  2008-07-08

4.  Unsupervised movement onset detection from EEG recorded during self-paced real hand movement.

Authors:  Bashar Awwad Shiekh Hasan; John Q Gan
Journal:  Med Biol Eng Comput       Date:  2009-11-04       Impact factor: 2.602

5.  A comparison of univariate, vector, bilinear autoregressive, and band power features for brain-computer interfaces.

Authors:  Clemens Brunner; Martin Billinger; Carmen Vidaurre; Christa Neuper
Journal:  Med Biol Eng Comput       Date:  2011-09-25       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.  Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

Authors:  Niccolò Mora; Ilaria De Munari; Paolo Ciampolini; José Del R Millán
Journal:  Med Biol Eng Comput       Date:  2016-11-17       Impact factor: 2.602

8.  Efficient human-machine control with asymmetric marginal reliability input devices.

Authors:  John H Williamson; Melissa Quek; Iulia Popescu; Andrew Ramsay; Roderick Murray-Smith
Journal:  PLoS One       Date:  2020-06-01       Impact factor: 3.240

9.  Novel hold-release functionality in a P300 brain-computer interface.

Authors:  R E Alcaide-Aguirre; J E Huggins
Journal:  J Neural Eng       Date:  2014-11-07       Impact factor: 5.379

10.  Decoding hand movement velocity from electroencephalogram signals during a drawing task.

Authors:  Jun Lv; Yuanqing Li; Zhenghui Gu
Journal:  Biomed Eng Online       Date:  2010-10-28       Impact factor: 2.819

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