100 tabular samples of synthetically generated Electroencephalography (EEG) features, designed for testing and optimizing closed-loop neuro-generative software architectures. The data provides pre-extracted Power Spectral Density (PSD) features ready for direct ingestion by machine learning classifiers. It was authored by Shubham Sunil Kumar and last updated on June 29, 2026.
Use Cases
- Testing machine learning classifiers like Support Vector Machines (SVM) based on pre-extracted Power Spectral Density (PSD) features.
- Optimizing artificial neural network (ANN) architectures for EEG signal processing using synthetic data.
- Benchmarking closed-loop neuro-generative software systems with simulated spatial-temporal brain embeddings.
Strengths
- Contains 100 tabular samples of synthetically generated data.
- Provides pre-extracted Power Spectral Density (PSD) features, bypassing dimensionally inflated raw time-series voltage matrices.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Data is synthetically generated, which may not fully represent real-world EEG signals.
Provenance
- Source
- Harvard Dataverse
- Collection Method
- Synthetically generated.
- Freshness
- Last updated 2026-06-29 12:50:23; freshness should be verified.