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Dissociating EEG sources linked to stimulus and response evaluation in numerical Stroop task using Independent
Ewa Beldzik1, Aleksandra Domagalik2, Wojciech Froncisz3
1Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland; Department of Molecular Biophysics, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland; Neurobiology Department, Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland.
Independent Component Analysis (ICA) identified posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC) as key neural sources for the N450 event-related potential (ERP) during a numerical Stroop task.
Area of Science:
- Neuroscience
- Cognitive Science
- Signal Processing
Background:
- Electroencephalography (EEG) is crucial for studying brain activity.
- Event-related potentials (ERPs) reflect neural processing of stimuli.
- Independent Component Analysis (ICA) offers advanced signal separation for EEG analysis.
Purpose of the Study:
- To identify neural sources contributing to the N450 ERP using ICA.
- To investigate the roles of specific brain regions in cognitive tasks.
- To analyze brain responses during a numerical Stroop task.
Main Methods:
- Dense-array EEG data collected from 20 participants performing a numerical Stroop task.
- Application of ICA for artifact removal and source separation.
- Clustering of identified neural sources and subsequent ERP analysis.
Main Results:
- ICA revealed two primary sources for the N450: posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC).
- PCC activity correlated with stimulus evaluation, showing prolonged activity in demanding trials and reduced activity before errors.
- ACC activity showed a post-response deflection and error-related negativity, indicating action-outcome evaluation.
Conclusions:
- PCC is involved in stimulus evaluation, while ACC evaluates action-outcome.
- Errors in the numerical Stroop task may stem from insufficient stimulus processing in the PCC.
- ICA is an effective method for ERP analysis, providing novel insights into brain potentials.
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