ArticlePsychophysiology2024
Evaluating the effectiveness of artifact correction and rejection in event-related potential research.
Article in Psychophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence-based analysis of visual electrophysiological signals for clinical interpretation support.Frontiers in neuroscience · 2026Review
- ICA-SFrontiers in neuroscience · 2026Article
- Disrupted Global Brain Dynamics in Adolescents With Comorbid Anxiety and Depression: Neural Mechanisms and Classification Based on EEG Microstates.Depression and anxiety · 2026Article
- An Electroencephalographic Investigation of the Impact of Eye Movements in a Memory Probe Task.Psychophysiology · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- Acute stress impairs target enhancement rather than distractor suppression in attention selection: evidence from the N2pc and PBMC psychology · 2025Article
- Electroencephalographic (EEG) analysis of hue perception differences between art and non-art majors: insights from the P2 and P3 components.BMC psychology · 2025Article
- Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals.NeuroImage · 2025Article
- Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals.bioRxiv : the preprint server for biology · 2025Article
- The role of conscious attention in auditory statistical learning: Evidence from patients with impaired consciousness.iScience · 2025Article
- Artifact filtering application to increase online parity in a communication BCI: progress toward use in daily-life.Frontiers in human neuroscience · 2025Article
- Dysfunctional Alpha Modulation as a Mechanism of Working Memory Impairment in Serious Mental Illness.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- EEG might be better left alone, but ERPs must be attended to: Optimizing the late positive potential preprocessing pipeline.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2024Article
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5 authors.
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Abstract
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.
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