Review of challenges associated with the EEG artifact removal methods
Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. How...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mumtaz, Wajid [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
Independent component analysis |
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| Übergeordnetes Werk: |
Enthalten in: Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood - Yan, Yinkun ELSEVIER, 2017, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:68 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.bspc.2021.102741 |
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| 520 | |a Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. | ||
| 520 | |a Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. | ||
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10.1016/j.bspc.2021.102741 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001480.pica (DE-627)ELV054477107 (ELSEVIER)S1746-8094(21)00338-4 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Mumtaz, Wajid verfasserin aut Review of challenges associated with the EEG artifact removal methods 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Independent component analysis Elsevier Challenges of artifact removal methods Elsevier EEG preprocessing methods Elsevier EEG artifact removal method Elsevier EEG artifact correction methods Elsevier Rasheed, Suleman oth Irfan, Alina oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:68 year:2021 pages:0 https://doi.org/10.1016/j.bspc.2021.102741 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 68 2021 0 |
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10.1016/j.bspc.2021.102741 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001480.pica (DE-627)ELV054477107 (ELSEVIER)S1746-8094(21)00338-4 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Mumtaz, Wajid verfasserin aut Review of challenges associated with the EEG artifact removal methods 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Independent component analysis Elsevier Challenges of artifact removal methods Elsevier EEG preprocessing methods Elsevier EEG artifact removal method Elsevier EEG artifact correction methods Elsevier Rasheed, Suleman oth Irfan, Alina oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:68 year:2021 pages:0 https://doi.org/10.1016/j.bspc.2021.102741 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 68 2021 0 |
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10.1016/j.bspc.2021.102741 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001480.pica (DE-627)ELV054477107 (ELSEVIER)S1746-8094(21)00338-4 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Mumtaz, Wajid verfasserin aut Review of challenges associated with the EEG artifact removal methods 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Independent component analysis Elsevier Challenges of artifact removal methods Elsevier EEG preprocessing methods Elsevier EEG artifact removal method Elsevier EEG artifact correction methods Elsevier Rasheed, Suleman oth Irfan, Alina oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:68 year:2021 pages:0 https://doi.org/10.1016/j.bspc.2021.102741 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 68 2021 0 |
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10.1016/j.bspc.2021.102741 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001480.pica (DE-627)ELV054477107 (ELSEVIER)S1746-8094(21)00338-4 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Mumtaz, Wajid verfasserin aut Review of challenges associated with the EEG artifact removal methods 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Independent component analysis Elsevier Challenges of artifact removal methods Elsevier EEG preprocessing methods Elsevier EEG artifact removal method Elsevier EEG artifact correction methods Elsevier Rasheed, Suleman oth Irfan, Alina oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:68 year:2021 pages:0 https://doi.org/10.1016/j.bspc.2021.102741 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 68 2021 0 |
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10.1016/j.bspc.2021.102741 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001480.pica (DE-627)ELV054477107 (ELSEVIER)S1746-8094(21)00338-4 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Mumtaz, Wajid verfasserin aut Review of challenges associated with the EEG artifact removal methods 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. Independent component analysis Elsevier Challenges of artifact removal methods Elsevier EEG preprocessing methods Elsevier EEG artifact removal method Elsevier EEG artifact correction methods Elsevier Rasheed, Suleman oth Irfan, Alina oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:68 year:2021 pages:0 https://doi.org/10.1016/j.bspc.2021.102741 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 68 2021 0 |
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Enthalten in Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood Amsterdam [u.a.] volume:68 year:2021 pages:0 |
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Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. |
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Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. |
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Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections. |
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