Multichannel EEG Compression: Wavelet-Based Image and Volumetric Coding Approach

Abstract

Abstract-In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram signals (EEG) are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of "lossy plus residual coding", consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different sampling rate and resolution. The proposed multichannel compression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately.

Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

“…In [15], we proposed the idea of arranging the multichannel EEG in the form of a matrix or tensor, exploiting the interand intra-channel correlations by matrix/tensor decompositions, and their potential application towards compression. Further, in [16], we developed compression algorithms using image/volumetric wavelet coders that supports progressive quality, progressive resolution, and guarantee a maximum distortion bound in L ∞ sense (cf. (1)).…”

“…Here we explore the use of matrix/tensor decompositions, and extend our earlier study [15] to design near-lossless compression algorithms for MC-EEG. First, as in [16], we represent MC-EEG in various multi-way forms, and apply matrix or tensor decompositions to exploit both spatial and temporal correlations simultaneously. We construct a simple and efficient coding procedure to compress the matrix/tensor decompositions.…”

“…We construct a simple and efficient coding procedure to compress the matrix/tensor decompositions. Similarly to the coders in [16], they support progressive quality and guarantee the maximum distortion bound (eq. (1)).…”

“…(1)). The compression schemes presented in this paper provide a substantially more favorable compression vs. distortion trade-off compared to the wavelet-based schemes proposed in [16]. This paper is organized as follows.…”

Near-Lossless Compression for Large Traffic Networks

“…Wavelet transforms have been widely used in compression related applications including images [29], [30] and medical data sets such as electroencephalogram (EEG) [31], [32]. Similar to DCT, wavelets also perform compression using a pre-specified basis set.…”