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

翻译：为探究大脑中的语音处理机制，线性模型常被用于建立脑信号与语音特征之间的关联。然而，这类线性模型难以胜任对大脑这种高度动态且复杂的非线性系统的建模。尽管近年来已发展出基于神经网络的非线性方法，但从未知被试的脑电图（EEG）中重构未观测刺激仍是一项极具挑战的任务。本文提出了一种名为ConvConcatNet的新方法，通过融合深度卷积神经网络与广泛连接操作，实现了从EEG到梅尔频谱图的重构。采用我们的ConvConcatNet模型，重构梅尔频谱图与目标梅尔频谱图之间的皮尔逊相关系数可达0.0420，在听觉EEG挑战赛任务2中位列第一。实现本文工作的代码与模型将开源至Github：https://github.com/xuxiran/ConvConcatNet