title: A channel differential EZW coding scheme for EEG data compression.
abstract: In this paper, a method is proposed to compress multichannel electroencephalographic (EEG) signals in a scalable fashion. Correlation between EEG channels is exploited through clustering using a k-means method. Representative channels for each of the clusters are encoded individually while other channels are encoded differentially, i.e., with respect to their respective cluster representatives. The compression is performed using the embedded zero-tree wavelet encoding adapted to 1-D signals. Simulations show that the scalable features of the scheme lead to a flexible quality/rate tradeoff, without requiring detailed EEG signal modeling.
authors: Department of Electrical and Computer Engineering, McGill University, Montréal, QC, Canada. vahid@cim.mcgill.ca
authors: Vahid R
authors: VR
authors: Dehkordi
authors: Hoda
authors: H
authors: Daou
authors: Fabrice
authors: F
authors: Labeau
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D044962:Data Compression / Q000379:methods*; D004569:Electroencephalography; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis