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Electroencephalogram (EEG) data compression is necessary for wirelessrecording applications to reduce the amount of data that needs to betransmitted. In this paper, an asymmetrical sparse autoencoder with a discretecosine transform (DCT) layer is proposed to compress EEG signals. The encodermodule of the autoencoder has a combination of a fully connected linear layerand the DCT layer to reduce redundant data using hard-thresholdingnonlinearity. Furthermore, the DCT layer includes trainable hard-thresholdingparameters and scaling layers to give emphasis or de-emphasis on individual DCTcoefficients. Finally, the one-by-one convolutional layer generates the latentspace. The sparsity penalty-based cost function is employed to keep the featuremap as sparse as possible in the latent space. The latent space data istransmitted to the receiver. The decoder module of the autoencoder is designedusing the inverse DCT and two fully connected linear layers to improve theaccuracy of data reconstruction. In comparison to other state-of-the-artmethods, the proposed method significantly improves the average quality scorein various data compression experiments.

Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer