The EEG data used in this work is the “Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions” from the Giga DB dataset completed by Jeong et al. (2020a) (link). The dataset included intuitive upper limb movement data from 25 subjects, who were required to perform three types of motor tasks in a total of 11 categories, including 6 directions of arm extension movement (up, down, left, right, front, back), 3 kinds of object grasping action (cup, card, ball) and 2 kinds of wrist-twisting action (left rotation, right rotation), each type of movement was randomly executed 50 times, corresponding to 11 movements designed to be associated with each segmental movement of the arm, hand, and wrist, rather than continuous limb movements. The dataset included not only EEG data but also magnetoencephalography (EMG) and electrooculogram (EOG) data, which are collected simultaneously in the same experimental setting while ensuring no interference between them. The data were acquired using a 60-channel EEG, 7-channel EMG, and 4-channel EOG. In the current work, only motor imagery EEG data were used, the EEG sensors were placed according to the international 10–20 system, and the sampling rate was set as 2,500 Hz. Our goal is to classify the motor imagery EEG of the three types of actions, so we selected forward extension of the arm, grasping the cup, and rotation of the wrist to the left from the three types of actions for the following study.
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