For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
15
views
113
references
 Top references
cited by
114
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      657
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Removal of Artifacts from EEG Signals: A Review

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

          Related collections

          Most cited references113

          • Record: found
          • Abstract: not found
          • Article: not found

          ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

          Zhaohua Wu, Norden Huang (2009)
          Article 0 comments Cited 1118 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Identifying true brain interaction from EEG data using the imaginary part of coherency.

            Guido Nolte, Ou Bai, Lewis A. Wheaton … (2004)
            The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. It is possible to reliably detect brain interaction during movement from EEG data. The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.
            Article 0 comments Cited 526 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.

              Theodore J Huppert, Solomon G Diamond, Maria A Franceschini … (2009)
              Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.
              Article 0 comments Cited 488 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 26 February 2019
                Publication date Collection: March 2019
                Volume: 19
                Issue: 5
                Electronic Location Identifier: 987
                Affiliations
                [1 ]Institute of Automation, Chinese Academy of Science, Beijing 100190, China; jx19950427@ 123456163.com
                [2 ]School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China; tianzean@ 123456126.com
                Author notes
                [* ]Correspondence: guibin.bian@ 123456ia.ac.cn
                Author information
                https://orcid.org/0000-0003-4708-2245
                Article
                Publisher ID: sensors-19-00987
                DOI: 10.3390/s19050987
                PMC ID: 6427454
                PubMed ID: 30813520
                SO-VID: 7eb14981-291b-4f9a-9a2a-3865cfeac699
                Copyright © © 2019 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 15 December 2018
                Date accepted : 21 February 2019
                Categories
                Subject: Review

                ScienceOpen disciplines: Biomedical engineering
                Keywords: electroencephalogram,artifact removal techniques,artifacts
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: electroencephalogram, artifact removal techniques, artifacts

                Comments

                Comment on this article

                scite_

                Similar content657

                See all similar

                Cited by114

                See all cited by

                Most referenced authors997

                See all reference authors