Overt Naming Task EEG Data with EOG Denoising

Electroencephalography (EEG) data from 40 participants in an overt naming experiment. The data has been processed using a denoising pipeline that removed gradient artifacts, ECG signals, and regressed out signals near the eyes and upper jaw. It was authored by Adrian Gilmore and last updated on April 25, 2026.

Use Cases

• Analyzing neural correlates of language production based on EEG signals from an overt naming task.
• Comparing denoising methodologies based on the described pipeline that removed gradient artifacts and ECG.
• Studying the impact of ocular and jaw muscle artifacts on EEG data based on the regression of signals near the eyes and upper jaw.
• Investigating brain oscillatory power changes related to behavioral improvement, as referenced in the associated research.

Strengths

• Data from 40 participants, providing a moderate sample size for analysis.
• Processed with a specific denoising pipeline for gradient artifact and ECG removal.
• Associated with a specific research context on repetition-related neural activity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.
• The description notes the data does not include an additional ICA denoising step present in a related dataset.

Provenance

Source

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, processed with a denoising pipeline.

Freshness

Last updated 2026-04-25 00:08:40; freshness should be verified.