Medical studies have shown that EEG of Alzheimer's disease (AD) patients is “slower” (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.

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A cross-coupled controller, designed to improve high-speed contouring accuracy independently of tracking accuracy in biaxial machine tool feed drive servomechanisms, is presented here. The controller parameters depend on the instantaneous slope of the desired contour and hence vary with time for curved contours, resulting in a time-varying controller. An approximate stability analysis of the controller is presented. The proposed controller is evaluated experimentally on a microcomputer controlled two-axis positioning table and compared to a more traditional uncoupled controller. Controller performance is evaluated for straight line, cornering and circular contours at feed rates varying from 2.25 m/min to 7.2 m/min. The experimental results show that the proposed controller reduces contouring error as compared to the uncoupled controller and leaves the tracking error practically unchanged. The cross-coupled controller is simple to implement and is practical.

Abstract-A novel near-lossless compression algorithm for multi-channel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. Multi-channel EEG is represented in suitable multi-way (multi dimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multi-way 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 followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between 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 five-fold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

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.

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