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High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans.

G Lomele¹, T Lencioni¹, S D'Ambrosio^1,2,3

¹IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy.

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|April 18, 2026

Summary

This summary is machine-generated.

This study presents a new open-access dataset of synchronized electroencephalography (EEG) and electromyography (EMG) recordings during prehension movements. This resource aids research into neural control of hand actions and sensorimotor integration.

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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Area of Science:

Neuroscience
Biomedical Engineering
Motor Control Research

Background:

Cortical control of prehension movements necessitates synchronized neural and muscular activity data.
Existing datasets often lack the synchronized, high-density neural and multi-muscle recordings required for detailed analysis.

Purpose of the Study:

To introduce a novel, open-access dataset of synchronized electroencephalography (EEG) and electromyography (EMG) recordings during prehension movements.
To facilitate research into the neural mechanisms underlying complex hand movements and sensorimotor integration.

Main Methods:

Collected high-density EEG (64 channels) and multi-muscle EMG (13 upper-limb muscles) from 14 healthy participants.
Recorded data during visually guided prehension tasks involving precision grip, whole-hand power grasp, and an unconventional grip.
Utilized a custom sensorized device for precise timestamping of action events and divided trials into dynamic and isometric phases.

Main Results:

The dataset captures synchronized neural (EEG) and muscular (EMG) activity during various prehension grips.
Enables analysis of muscle synergy patterns alongside EEG features for cortico-muscular interaction studies.
Provides detailed temporal data across dynamic and isometric phases of prehension movements.

Conclusions:

This comprehensive dataset is a valuable resource for studying the neural control of complex hand movements.
Supports research in sensorimotor integration, adaptive brain-computer interfaces, and motor control.
Offers potential applications for neuroscientists, engineers, and clinicians in rehabilitation and understanding motor disorders.