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A space-time channel coding technique is presented for overcoming turbulence-induced fading in an atmospheric optical heterodyne communication system that uses multiple transmit and receive apertures. In particular, a design criterion for minimizing the pairwise probability of codeword error in a space-time code (STC) is developed from a central limit theorem approximation. This design criterion maximizes the mean-to-standard-deviation ratio of the received energy difference between codewords. It leads to STCs that are a subset of the previously reported STCs for Rayleigh channels, namely those created from orthogonal designs. This approach also extends to other fading channels with independent, zero-mean path gains. Consequently, for large numbers of transmit and receive antennas, STCs created from orthogonal designs minimize the pairwise codeword error probability for this larger class of fading channels

The time-varying dynamics of epileptic seizures and the high inter-individual variability make their detection difficult. Osorio et al. [Osorio, I, Frei, MG, Wilkinson, SB. Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsia 1998;39(6):615-27] developed an algorithm that has had success in detecting seizures. We present a new strategy for adapting this algorithm or other algorithms to an individual's seizure fingerprint using both seizure and non-seizure training segments and a novel performance criterion that directly incorporates the non-linearity and lack of differentiability of the algorithm. The joint optimization of a linear filter chosen from a bank of candidate filters and of a percentile used in order statistic filtering provides an empirical solution that is both practical and useful, which should translate into improved sensitivity, specificity and detection speed. This premise is strongly supported by the results obtained in a large validation study and the examples illustrated in this article. This strategy is generalizable to other detection algorithms with modular architecture and spectral filters.

Abstract. The removal of ocular artifact from scalp electroencephalograms (EEGs) is of considerable importance for both the automated and visual analysis of underlying brainwave activity. Traditionally, subtraction techniques use linear regression to estimate the influence of eye movements on the electrodes of interest. These methods are based on the assumption that the underlying brainwave activity is uncorrelated when, in general, it is not. Furthermore, regression methods assume that the ocular artifact propagation is frequency independent, i.e. all waveforms of the ocular artifact propagate similarly. In this paper, we examine relaxing these assumptions by using a more general autoregressive (AR) moving average (MA) exogenous (X) model and the extended least squares (ELS) algorithm to remove ocular artifact. We demonstrate that in some cases this general ARMAX model can decrease ocular artifact not removable by standard regression techniques. We also show that the incorporation of a forgetting factor to exponentially weight past data can improve ocular artifact removal even for the traditional subtraction method.