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arXiv · 2401.04965

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
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Abstract

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

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Xiran Xu, Bo Wang, Yujie Yan, Haolin Zhu, Zechen Zhang, Xihong Wu, Jing Chen. 2024-01-10. ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG. https://doi.org/10.1109/icasspw62465.2024.10626859

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