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

Tópicos

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Resumo

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 660g), 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').

Resumo Limpo

wayeegg dataset design allow critic test techniqu decod sensat intent action scalp eeg record human perform graspandlift task twelv particip perform lift seri object weight g surfac friction sandpap sued silk surfac chang unpredict trial thus enforc chang fingertip forc coordin total trial particip cu reach object grasp thumb index finger lift hold coupl second put back support surfac releas last return hand design rest posit record eeg channel emg five arm hand muscl d posit hand object forcetorqu contact plate trial provid event time eg object liftoff measur character behaviour eg peak grip forc

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