Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer follod by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error beten original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lor average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.
['Justin Dauwels', 'K. Srinivasan', 'M.R.S. Reddy', 'A. Cichocki']
I am writing a research paper on a idea Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions
[{"from": "human", "value": "I am writing a research paper on a idea Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of \u201clossy plus residual coding,\u201d consisting of a matrix/tensor decomposition-based coder in the lossy layer follod by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error beten original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lor average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm."}]