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, while EEG signals were recorded. The participants were divided equally into two age groups: 10 elderly adults aged 55-72 and 10 young adults aged 19-33. The processed data, stored in MNE .fif files, was contributed by Nikita Frolov.

Use Cases

• Classifying left-hand versus right-hand motor execution based on EEG signal patterns.
• Analyzing age-related differences in cortical activity during fine motor tasks.
• Developing models to detect or remove ocular and cardiac artifacts from EEG data.
• Studying the temporal dynamics of motor-related brain activity over an 8-second window.

Strengths

• Includes data from two distinct age groups with 10 subjects each, enabling comparative analysis.
• Signals were processed using established methods (Butterworth filter, Notch filter, ICA) to remove artifacts.
• The experimental design randomized task sequence and inter-trial intervals to reduce training effects.

Limitations

• The sample size of 20 subjects may limit the statistical power for some analyses.
• Column-level documentation is absent; field semantics must be inferred after download.
• 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) and processed with MNE Python.