EEG Signals from 26 Learners Under Intrinsic and Extrinsic Interest Conditions

26 participants completed learning tasks under autonomy-support and autonomy-deprivation paradigms while 16-channel EEG signals were recorded. The dataset includes power features and peak-frequency indices across five canonical frequency bands, packaged in a 7.9 GB ZIP file. Author jingfei Hou published the dataset on figshare under a CC-BY-4.0 license, with a last update in April 2026.

Use Cases

• Classifying learner interest states based on EEG power features and peak-frequency indices.
• Investigating neural mechanisms of learning under autonomy-support versus autonomy-deprivation conditions.
• Developing brain-computer interface applications for adaptive learning systems based on interest detection.
• Comparing the performance of classical machine learning models like SVM and k-NN on neurophysiological data.

Strengths

• Data was collected from 26 participants in realistic learning scenarios, providing ecological validity.
• Includes EEG signals from two distinct experimental paradigms (intrinsic and extrinsically assigned interest).
• Machine learning validation reported mean classification accuracies of 98.32% and 96.42% for the two paradigms.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific modeling tasks.
• The dataset size of 7.9 GB suggests a medium-scale collection; the exact number of observations per participant is not specified.

Provenance

Source

figshare, author jingfei Hou.

Collection Method

EEG signals were synchronously recorded from 26 participants during designed learning tasks under two experimental paradigms.

Freshness

Last updated 2026-04-25 15:33:50; freshness should be verified.