EEG, Eye-Tracking, and Survey Data for AI-Assisted Design Cognition Study

Data from a study of 39 EEG and 48 other participants supports replication of statistical results on information construal level in AI-assisted design. The dataset includes 12 CSV files covering EEG alpha-ERSP, eye-tracking for different visual zones, subjective questionnaires, prompt coding, and expert design evaluations. It was authored by ZHE LIANG and last updated on 2026-05-22.

Use Cases

• Analyze neural correlates of design tasks based on EEG alpha-ERSP data for futuristic and cultural tasks.
• Study visual attention patterns based on eye-tracking data for whole stimulus, formula zone, and descriptor zone.
• Correlate subjective workload and usability perceptions with design outcomes based on NASA-TLX and USE questionnaire data.
• Investigate the relationship between prompt construal level coding and expert design evaluation scores.

Strengths

• Includes data from 39 EEG participants and 48 participants for other measures, providing a multi-modal foundation.
• Contains 12 distinct data files covering EEG, eye-tracking, questionnaires, and evaluations for comprehensive analysis.
• Explicitly supports replication of all statistical results reported in the associated manuscript.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 53.0 KB, indicating a limited scope for large-scale machine learning.

Provenance

Source

figshare

Collection Method

Collected via EEG, eye-tracking hardware, and subjective questionnaires during a controlled study.

Freshness

Last updated 2026-05-22 06:56:25; freshness should be verified.