Disclosed is a deep learning based artificial electroencephalogram signal generating method and an apparatus thereof. The deep learning based artificial electroencephalogram signal generating apparatus comprises a processor and a memory connected with the processor. The memory can store program commands executed by the processor to preprocess an electroencephalogram signal of a target user to acquire an imaginary motion electroencephalogram signal, allows a generator to use a random noise as an input to generate a virtual electroencephalogram signal based on a generative adversarial network model, allows a discriminator to discriminate similarity between the virtual electroencephalogram signal and the acquired imaginary motion electroencephalogram signal, and allows brain-interface device to determine, when the similarity between the virtual electroencephalogram signal and the imaginary motion electroencephalogram signal acquired by the generative adversarial network model satisfies a preset value, the virtual electroencephalogram signal generated by the generator as an artificial electroencephalogram signal dataset for classifying the imaginary motion electroencephalogram signal.
Deep learning based artificial electroencephalogram signal generation method and apparatus