40 participants performed an overt naming experiment while their brain activity was recorded. Source estimated EEG data has been processed to remove gradient artifacts, ECG signals, and regressed signals near the eyes and upper jaw. This version does not include the independent component analysis (ICA) denoising step present in a related dataset.
Use Cases
- Modeling the neural correlates of language production based on denoised EEG signals.
- Comparing neural activity patterns between different denoising pipelines (e.g., regression vs. ICA).
- Analyzing the impact of ocular and cardiac artifacts on EEG data quality in cognitive tasks.
- Training machine learning models to classify brain states during overt speech tasks.
Strengths
- Data from 40 participants provides a moderate sample size for analysis.
- Specific denoising pipeline details are provided, including gradient artifact and ECG removal.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- The description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- Gilmore, Adrian; Data and code from: Repetition-related reductions in neural activity support improved behavior through increases in oscillatory power
- Collection Method
- EEG data recorded from participants in an overt naming experiment, processed with a specific denoising pipeline.
- Freshness
- Last updated 2026-04-25 00:08:52; freshness should be verified.