Efficient multichannel EEG compression by optimal tensor truncation

Samiran Das, Chirag Kyal

Abstract: Electroencephalography (EEG) signal is a crucial biomedical signal that helps assess the psychological, neural, and cognitive conditions of the human brain. However, the huge size of modern multi-channel EEG signals sampled at a high rate create difficulties in storage, transmission, and processing. Efficient compression methods obtain a compact form of the signal and mitigate these issues. In this paper, we propose an optimal tensor truncation method for performing compression of the data. In our proposed work, we first reshape the multi-channel EEG signal as a tensor and initially identify the optimum size of the compressed tensor. In the next stage, we perform tensor decomposition and obtain a core tensor of significantly smaller size to represent the information contained in the tensor. The core tensor obtained by the process captures the details of the original signal and leads to high compression ratio. We reconstruct the original tensor by transforming the core tensor with the transposed factor matrices on the decoder side. We have experimented with the proposed compression method on several online available datasets using several performance measures. From the experiments mentioned above, we conclude that we obtain better compression performance compared to the state of the art approaches.