Portable EEG Data for Cognitive Load Assessment in University Students and Teachers

A pilot study dataset from figshare, last updated in March 2026, exploring cognitive load assessment using portable EEG. The data, processed with the MNE-Python toolkit, includes EEG recordings from senior university students, novice teachers, and experienced teachers during task performance. It was authored by María Consuelo Sáiz-Manzanares and shared under a CC-BY-4.0 license.

Use Cases

• Compare pre-task cognitive load perceptions between different participant groups (students, novice teachers, experienced teachers) as described in the study aims.
• Analyze differences in peak power per brain band (Delta, Theta, Alpha, Beta, Gamma) by participant type during task execution.
• Perform cluster analysis to identify unlabeled groupings in the EEG data and correlate them with participant type.
• Investigate frequencies of maximum activation in Delta wave brain channels across the defined participant groups.

Strengths

• Data was processed using the established MNE-Python toolkit for EEG analysis.
• The study design explicitly compares three distinct participant groups (senior students, novice teachers, experienced teachers).
• The dataset is openly shared under a permissive CC-BY-4.0 license.

Limitations

• The dataset is small (2.2 MB), suggesting limited sample size or features.
• 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

figshare

Collection Method

Collected via a pilot study using a portable EEG device on university students and teachers.

Freshness

Last updated 2026-03-19 06:34:48