Key result
PCA-based algorithm achieves ~10-fold compression of EEG signals with minimal distortion.
Why the study?
Recording biomedical measurement procedures requires large amounts of data to monitor health, with EEG data being particularly demanding in size and signal behavior.
A novel near-lossless compression algorithm using PCA for multichannel EEG signals achieves a compression ratio of ~10 with <0.01% distortion, reducing memory requirements.
May enable efficient EEG data handling in monitoring; leaves open validation for clinical cardiovascular applications.
In many biomedical measurement procedures, it is important to record a huge amount of data, to monitor the state of health of a subject. In such a context, electroencephalograph (EEG) data are one of the most demanding in terms of size and signal behavior. In this paper, we propose a near-lossless compression algorithm for EEG signals able to achieve a compression ratio in the order of 10 with a root-mean-square distortion less than 0.01%. The proposed algorithm exploits the fact that Principal Component Analysis is usually performed on EEG signals for denoising and removing unwanted artifacts. In this particular context, we can consider this algorithm as a good tool to ensure the best information of the signal beside an efficient compression ratio, reducing the amount of memory necessary to record data.
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Campobello et al. (2021) studied EEG signal compression. Near-lossless compression algorithm using Principal Component Analysis was evaluated on Compression ratio and root-mean-square distortion. A near-lossless compression algorithm using Principal Component Analysis for EEG signals achieved a compression ratio of approximately 10 with a root-mean-square distortion of less than 0.01%.
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