Electroencephalogram Sensor Data Compression Using An Asymmetrical
  Sparse Autoencoder With A Discrete Cosine Transform Layer
Xin Zhu Hongyi Pan Shuaiang Rong Ahmet Enis Cetin
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.