Literature DB >> 17169606

EMG and EOG artifacts in brain computer interface systems: A survey.

Mehrdad Fatourechi1, Ali Bashashati, Rabab K Ward, Gary E Birch.   

Abstract

It is widely accepted in the brain computer interface (BCI) research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Electrooculography (EOG) and electromyography (EMG) artifacts are considered among the most important sources of physiological artifacts in BCI systems. Currently, however, there is no comprehensive review of EMG and EOG artifacts in BCI literature. This paper reviews EOG and EMG artifacts associated with BCI systems and the current methods for dealing with them. More than 250 refereed journal and conference papers are reviewed and categorized based on the type of neurological phenomenon used and the methods employed for handling EOG and EMG artifacts. This study reveals weaknesses in BCI studies related to reporting the methods of handling EMG and EOG artifacts. Most BCI papers do not report whether or not they have considered the presence of EMG and EOG artifacts in the brain signals. Only a small percentage of BCI papers report automated methods for rejection or removal of artifacts in their systems. As the lack of dealing with artifacts may result in the deterioration of the performance of a particular BCI system during practical applications, it is necessary to develop automatic methods to handle artifacts or to design BCI systems whose performance is robust to the presence of artifacts.

Mesh:

Year:  2006        PMID: 17169606     DOI: 10.1016/j.clinph.2006.10.019

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  67 in total

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

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Review 9.  Electromyogenic artifacts and electroencephalographic inferences.

Authors:  Alexander J Shackman; Brenton W McMenamin; Heleen A Slagter; Jeffrey S Maxwell; Lawrence L Greischar; Richard J Davidson
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10.  Decoding hand movement velocity from electroencephalogram signals during a drawing task.

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Journal:  Biomed Eng Online       Date:  2010-10-28       Impact factor: 2.819

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