@inproceedings{a7f7235db0df4e63ab68c257509ec6f7,
title = "ConvConcatNet: A Deep Convolutional Neural Network to Reconstruct Mel Spectrogram from the EEG",
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 illequipped 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-spectrograms 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",
keywords = "ConvConcatNet, EEG, Mel spectrogram reconstruction, unseen stimuli, unseen subject",
author = "Xiran Xu and Bo Wang and Yujie Yan and Haolin Zhu and Zechen Zhang and Xihong Wu and Jing Chen",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSPW62465.2024.10626859",
language = "English",
series = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "113--114",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
address = "United States",
}