Covert Naming Task EEG Data from 25 Participants

25 participants' raw and minimally processed scalp EEG data from a covert naming experiment. The data includes files where MR gradient artifacts and ballistocardiogram effects have been removed, along with sensor location files for each electrode. The dataset was authored by Adrian Gilmore and last updated on April 25, 2026.

Use Cases

• Analyzing neural correlates of covert language production based on EEG recordings.
• Studying the effects of artifact removal (MR gradient, ballistocardiogram) on EEG signal quality.
• Investigating relationships between sensor location and brain activity patterns during a naming task.
• Developing or validating EEG preprocessing pipelines for cognitive experiments.

Strengths

• Includes data from 25 participants, providing a basis for group-level analysis.
• Contains both raw and minimally processed data, allowing comparison of preprocessing steps.
• Sensor location files are provided for spatial analysis of EEG signals.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and overall data size are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Data and code from: Repetition-related reductions in neural activity support improved behavior through increases in oscillatory power

Collection Method

Collected from participants in a covert naming experiment using scalp EEG.

Freshness

Last updated 2026-04-25 00:08:27; freshness should be verified.