Key result
Fast DCT algorithms enable real-time lossy EEG compression with low computational complexity.
Fast DCT algorithms provide an efficient method for lossy EEG data compression in real-time embedded systems requiring low computational complexity.
May reduce EEG transmission bandwidth in embedded devices; leaves open clinical validation before practice adoption.
Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.
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Birvinskas et al. (2014) studied EEG data compression. Fast Discrete Cosine Transform (DCT) algorithms was evaluated. Fast Discrete Cosine Transform (DCT) algorithms can be used for lossy EEG data compression in real-time embedded systems requiring low computational complexity and high speed.
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