Resting-state EEG Microstates and Cognitive Scores for 60 MCI and Healthy Control Subjects

60 adults (30 with Mild Cognitive Impairment, 30 healthy controls) underwent resting-state EEG and cognitive testing. The dataset includes EEG microstate parameters (Global Explained Variance, duration, occurrence, coverage) for seven canonical classes and scores from cognitive assessments (MMSE, MoCA, Raven's Progressive Matrices, Benton Facial Recognition Test). The data was contributed by Ceyhun Sayman and last updated in May 2026.

Use Cases

• Train models to classify MCI status based on EEG microstate characteristics like Global Explained Variance.
• Investigate correlations between specific microstate classes (e.g., B and C) and performance on fluid intelligence tests like Raven's Progressive Matrices.
• Analyze the temporal dynamics of neural networks through microstate duration, occurrence, and coverage parameters.

Strengths

• Includes data from 60 subjects with a balanced case-control design (30 MCI, 30 HC).
• Links millisecond-scale EEG microstate dynamics to multiple standardized cognitive assessments (MMSE, MoCA, RPM, BFRT).
• Analysis framework controlled for demographic covariates (age, education, sex) and used FDR correction.

Limitations

• Dataset is very small at 14.9 KB, indicating limited scope and sample size.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for some modeling tasks.

Provenance

Source

figshare

Collection Method

Resting-state, eyes-closed EEG was recorded from participants, and microstate parameters were calculated by mapping to canonical Metamaps2023 templates.

Freshness

Last updated 2026-05-20 05:50:17; freshness should be verified.