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1.
Psychophysiology ; 61(5): e14511, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38165059

RESUMO

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods in minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.


Assuntos
Eletroencefalografia , Potenciais Evocados , Humanos , Masculino , Feminino , Eletroencefalografia/métodos , Artefatos , Piscadela , Processamento de Sinais Assistido por Computador , Algoritmos
2.
bioRxiv ; 2023 Dec 12.
Artigo em Inglês | MEDLINE | ID: mdl-37745415

RESUMO

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

3.
Res Sq ; 2020 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-32743566

RESUMO

Electroencephalogram (EEG) recordings provide a valuable, noninvasive methofd for measuring human brain activity. This protocol modifies our general protocol for EEG recording (Farrens et al., 2019) for use during the COVID-19 pandemic. It was created with the help of numerous experts, and it specifies a clear set of steps for interacting with research participants, using personal protective equipment (PPE), and disinfecting equipment, all with the goal of reducing the COVID-19 risks for both laboratory personnel and participants. It focuses on the use of EEG in relatively simple research studies of adults who can easily understand and follow instructions, yet can be readily adapted for studies using other types of EEG experiments or other participant populations.

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