EEG Signals from Fine Motor Tasks in Young and Elderly Adults

20 healthy right-handed subjects performed 60 fine motor tasks each, cued by audio commands. The dataset contains preprocessed EEG epochs from 10 elderly adults (aged 55-72) and 10 young adults (aged 19-33), with data stored in MNE .fif files. The data was processed using the MNE Python package, including filtering and artifact removal via Independent Component Analysis.

Use Cases

• Classifying age group based on motor-related EEG activity patterns.
• Analyzing differences in cortical activity between left-hand and right-hand fine motor tasks.
• Developing models to detect or remove ocular and cardiac artifacts from EEG signals.
• Studying the temporal dynamics of motor execution and preparation from time-locked EEG epochs.

Strengths

• Includes data from two distinct age groups with 10 subjects each, enabling comparative analysis.
• Data is preprocessed using standardized methods (Butterworth filter, Notch filter, ICA) with artifact-free epochs selected.
• Experimental design includes randomization of task sequence and intervals to reduce training or preparation effects.

Limitations

• 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.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

EEG signals were acquired from subjects performing audio-cued fine motor tasks (squeezing a fist) in a controlled experiment.