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: this https URL

利用神经网络和深度卷积神经网络方法，本研究提出了一种重建脑电图中语音信号的新方法 ConvConcatNet，其中利用深度卷积神经网络和广泛的拼接操作相结合。在我们的 ConvConcatNet 模型中，重建的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到 0.0420，在听觉脑电挑战的任务 2 中排名第一。