Electroencephalogram (EEG) data compression is necessary for wireless
recording applications to reduce the amount of data that needs to be
transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete
cosine transform (DCT) layer is proposed to compress EEG signals. The encoder
module of the autoencoder has a combination of a fully connected linear layer
and the DCT layer to reduce redundant data using hard-thresholding
nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding
parameters and scaling layers to give emphasis or de-emphasis on individual DCT
coefficients. Finally, the one-by-one convolutional layer generates the latent
space. The sparsity penalty-based cost function is employed to keep the feature
map as sparse as possible in the latent space. The latent space data is
transmitted to the receiver. The decoder module of the autoencoder is designed
using the inverse DCT and two fully connected linear layers to improve the
accuracy of data reconstruction. In comparison to other state-of-the-art
methods, the proposed method significantly improves the average quality score
in various data compression experiments.

本文提出了一种具有离散余弦变换 (DCT) 层的非对称稀疏自编码器，用于压缩脑电图 (EEG) 信号，并通过稀疏惩罚损失函数在潜空间中保持特征图的稀疏性，最终使用解码器模块中的反 DCT 和两个完全连接的线性层来提高数据重构的准确性，与其他现有方法相比，在各种数据压缩实验中显著提高了平均质量得分。