Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction.

Matthew D Luciw,Ewa Jarocka,Benoni B Edin

doi:10.1038/sdata.2014.47

Abstract

WAY-EEG-GAL is a dataset designed to allow critical tests of techniques to decode sensation, intention, and action from scalp EEG recordings in humans who perform a grasp-and-lift task. Twelve participants performed lifting series in which the object’s weight (165, 330, or 660 g), surface friction (sandpaper, suede, or silk surface), or both, were changed unpredictably between trials, thus enforcing changes in fingertip force coordination. In each of a total of 3,936 trials, the participant was cued to reach for the object, grasp it with the thumb and index finger, lift it and hold it for a couple of seconds, put it back on the support surface, release it, and, lastly, to return the hand to a designated rest position. We recorded EEG (32 channels), EMG (five arm and hand muscles), the 3D position of both the hand and object, and force/torque at both contact plates. For each trial we provide 16 event times (e.g., ‘object lift-off’) and 18 measures that characterize the behaviour (e.g., ‘peak grip force’).

Highlights

The idea of extracting signals related to object manipulation from EEG recordings in humans seems reasonable given that even basic motor tasks engage large parts of the human cortex[1]
We present a dataset that allows critical evaluations of the utility of EEG signals for prosthetic control of object manipulation
The correct completion of the GAL task depends on multimodal sensory activity correlated with specific events such as object contact, lift-off, and replacement. This control policy, in which feedforward control routines operate between sensed discrete events, is known as the Discrete Event Sensory Control policy (DESC; refs 7–9)

Summary

Background & Summary

The idea of extracting signals related to object manipulation from EEG recordings in humans seems reasonable given that even basic motor tasks engage large parts of the human cortex[1] It is, not known how much information can be decoded from EEG. The correct completion of the GAL task depends on multimodal sensory activity correlated with specific events such as object contact, lift-off, and replacement This control policy, in which feedforward control routines operate between sensed discrete events, is known as the Discrete Event Sensory Control policy (DESC; refs 7–9). The object’s properties were several times changed in a manner that was unpredictable to the participant with respect to weight (165, 330, or 660 g), contact surface (sandpaper, suede, or silk), or both Such changes are known to induce specific modifications to the required muscle coordination.

Participants

Unexpected heavy