Figure 1.

Illustrates the proposed data augmentation method. It involves using the Short-Time Fourier Transform (STFT) to obtain time-frequency images of input EEG signals. Real data is used to train the Deep Convolutional Generative Adversarial Network-Gradient Penalty (DCGAN-GP) model to generate synthetic time-frequency images. These synthetic images are then mixed with real images in proportion and used to train a convolutional classifier to distinguish between left-hand and right-hand motor imagery (MI) actions.