ConvConcatNet: a deep convolutional neural network to reconstruct mel
  spectrogram from the EEG
Xiran Xu Bo Wang Yujie Yan Haolin Zhu Zechen Zhang Xihong Wu Jing Chen
To investigate the processing of speech in the brain, simple linear models
are commonly used to establish a relationship between brain signals and speech
features. However, these linear models are ill-equipped to model a highly
dynamic and complex non-linear system like the brain. Although non-linear
methods with neural networks have been developed recently, reconstructing
unseen stimuli from unseen subjects' EEG is still a highly challenging task.
This work presents a novel method, ConvConcatNet, to reconstruct mel-specgrams
from EEG, in which the deep convolution neural network and extensive
concatenation operation were combined. With our ConvConcatNet model, the
Pearson correlation between the reconstructed and the target mel-spectrogram
can achieve 0.0420, which was ranked as No.1 in the Task 2 of the Auditory EEG
Challenge. The codes and models to implement our work will be available on
Github: https://github.com/xuxiran/ConvConcatNet