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To investigate the processing of speech in the brain, simple linear modelsare commonly used to establish a relationship between brain signals and speechfeatures. However, these linear models are ill-equipped to model a highlydynamic and complex non-linear system like the brain. Although non-linearmethods with neural networks have been developed recently, reconstructingunseen stimuli from unseen subjects' EEG is still a highly challenging task.This work presents a novel method, ConvConcatNet, to reconstruct mel-specgramsfrom EEG, in which the deep convolution neural network and extensiveconcatenation operation were combined. With our ConvConcatNet model, thePearson correlation between the reconstructed and the target mel-spectrogramcan achieve 0.0420, which was ranked as No.1 in the Task 2 of the Auditory EEGChallenge. The codes and models to implement our work will be available onGithub: https://github.com/xuxiran/ConvConcatNet

ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG