Source estimated EEG data from 40 participants in an overt naming experiment. The data has been processed using a complete denoising procedure including gradient artifact and ECG removal, plus regression of signals near the eyes and upper jaw. The dataset was authored by Adrian Gilmore and last updated on April 25, 2026.
Use Cases
- Analyze neural correlates of language production based on EEG signals from an overt naming task.
- Train models to filter physiological artifacts based on the described EOG and EMG regression procedures.
- Study event-related neural dynamics around response times using data where ICA components summing to 90% of variance have been removed.
- Benchmark denoising algorithms against a dataset processed with gradient artifact and ECG removal.
Strengths
- Data from 40 participants provides a moderate sample size for cognitive neuroscience studies.
- Comprehensive denoising includes gradient artifact removal, ECG removal, and regression of ocular and jaw muscle signals.
- ICA components accounting for 90% of variance around response times have been removed, likely improving signal quality for event-related analysis.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for large-scale modeling.
Provenance
- Source
- Gilmore, Adrian; Data and code from: Repetition-related reductions in neural activity support improved behavior through increases in oscillatory power
- Collection Method
- Source estimated EEG data from participants in an overt naming experiment using a complete denoising procedure.
- Freshness
- Last updated 2026-04-25 00:09:19; freshness should be verified.