Data augmentation method based on RBSAGAN
A technology of data and quantity, applied in the field of using deep learning method to generate EEG signals of motor imagery, can solve the problems of missing features, limited feature information of EEG signals, not making full use of signal timing features, etc. Characteristic information is not comprehensive, and the effect of change is realized
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[0028]The experiment of the present invention is conducted in the following hardware environment: 14 kernel Intel Xeon E5-26832.00Hz CPU and 8GB memory GeForce 1070GPU. All neural networks are implemented using a Pytorch framework.
[0029]The data used in the present invention is "BCI Competition IV 2A" public data set. The electromal signal is 9 -20-conducting cap acquisition of 250 Hz through a specification of 10-20 system. 9 subjects perform four categories of motion imagination tasks: left hand, right hand, foot, tongue. Each subject for two-day experiment, containing 288 groups of experiments per day, a total of 576 groups of experiments. The electromal signal is filtered through a band pass filter and 50 Hz notch filter by 0.5 Hz to 100 Hz. Each experiment appears at 2S, the direction of the arrow is left, right, upper or lower (corresponding to the four types of tasks left hand, right hand, tongue or foot), and maintains 1.25s, the subject is in the direction of the arrow disp...
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