Neural network based near- lossless compression of EEG signals with non uniform quantization

Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach

The successful implementation of neural network-based EEG signal compression has led to significant cost reductions in data transmission. However, a major obstacle in this process arises from the decline in performance when compressing EEG signals from multiple subjects. This challenge arises due to the notable feature shift of EEG signals between subjects, which poses an impediment to the neural network's efficient concurrent acquisition of information from multiple subjects. To address this limitation and enable more effective utilization of data for improving the performance on target domain, we propose a Domain Adaptation (DA) framework based on LSTM-autoencoder. Our experiments encompassed the following: (1) A comparison between LSTM-autoencoder, GRU-autoencoder, and the commonly used convolutional autoencoder (CAE) in EEG compression. (2) A comparison between our proposed DA method and the MMD-based DA method, as well as Fine-tuning transfer learning. The results demonstrate the following: (1) LSTM-autoencoder outperforms other models in both subject-specific and cross-subject scenarios. (2) Using transfer learning improves the performance of LSTM-autoencoder on the target subject. (3) Our proposed method outperforms maximum mean discrepancy (MMD)-based domain adaptation and fine-tuning approaches, resulting in a more significant enhancement.

EEG compression of scalp recordings based on dipole fitting

A novel technique for electroencephalogram (EEG) compression is proposed in this paper. This technique models the intrinsic dependence inherent between the different EEG channels. It is based on methods borrowed from dipole fitting that is usually used in order to find a solution to the classic problems in EEG analysis: inverse and forward problems. To compress the EEG signals, the forward model based on approximated source dipoles is first used to provide an approximation of the recorded signals. Then, (based on a smoothness factor) appropriate coding techniques are suggested to compress the residuals of the fitting process. Results show that this technique works well for different recordings and for different patients, and is even able to provide near-lossless compression for certain types of recordings.

Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.