Electroencephalography signal classification is essential for the diagnosis and monitoring of neurological disorders, with significant implications for patient treatment. Despite the progress made, existing methods face challenges such as capturing the complex dynamics of Electroencephalogram (EEG) signals and generalizing across diverse patient populations. In this study, the graph attention network and the transformer model are integrated for EEG signal classification, leveraging the enhanced capability to dynamically compute attention weights and adapt to the variable relevance of brain regions. The proposed approach is capable of modeling the intricate relationships within EEG activities by learning context-dependent attention scores. We conducted a comprehensive evaluation of the proposed approach comparing with the state-of-the-art algorithms. Experimental outcomes show that it surpasses the competing models. The superior performance is attributed to the proposed approach's dynamic attention mechanism, which better captures the nuanced patterns in EEG signals across different subjects and seizure types. In the experiments, the CHB-MIT dataset was exploited, which served as a benchmark for evaluating the performance of the proposed framework in distinguishing interictal, ictal, and normal EEG patterns. The results prove the usefulness of our work in advancing EEG signal classification. The findings suggest that the combination of graph attention and self-attention mechanisms is a promising approach for improving the accuracy and reliability of EEG-based diagnostics, potentially improving the management of neurological disorders.

Many of the previously-developed multiple-access detection algorithms have assumed knowledge of the time delay, the amplitude, and the phase of each user. This paper presents methods of estimating the amplitudes and phases, both when the time delays are known and when the delays are unknown. In neither case are the transmitted bits assumed to be known. The estimators are unbiased and consistent, and each has a computational complexity linear in the number of users. Simulations are provided to demonstrate performance.>

The problem of simultaneously detecting the information bits and estimating signal amplitudes and phases in a K-user asynchronous direct-sequence spread-spectrum multiple-access communication system is addressed. The joint maximum-likelihood (ML) estimator has a computational complexity that is exponential in the total number of bits transmitted and thus does not represent a practical solution to the problem. An estimator that combines a suboptimum tree-search algorithm with a recursive least-squares estimator of complex signal amplitude is considered. The complexity of this estimator is O(K/sup 2/) computations per decoded bit, and its performance is very close to that of the joint ML receiver. This receiver has the advantage that the transmitted signal powers and phases are extracted from the received signal in an adaptive fashion without using a test sequence.>

The optimal previsualized image vector quantization method for compressing digital images to a bit rate of 0.75 bpp or below with moderately low to very low subjective distortion is presented. The encoding method incorporates a visual model as part of the distortion measure. By modeling the quantization noise as an additive signal-dependent noise process, an optimum pre- and postprocessing system, which minimizes the mean-squared error measured inside the visual model, is derived. The analysis of the system performance and a coordinate descent design algorithm are discussed. A set of experiments was conducted using the optimum system, and the results were compared to those obtained by other methods. The study shows that the images quantized by the method presented exhibit much less sawtooth, blocking, and contouring effects and higher subjective quality. Images of surprising quality have been produced by this method at a bit rate of about 0.1 bpp with a compression ratio of 80:1 relative to a normal 8 bpp original.>

Two adaptive step-size gradient adaptive filters are presented. The step sizes are changed using a gradient descent algorithm designed to minimize the squared estimation error. The first algorithm uses the same step-size sequence for all the filter coefficients, whereas the second algorithm uses different step-size sequences for different adaptive filter coefficients. An analytical performance analysis of the first algorithm is also presented. Analyses and experiments indicate that (1) the algorithms have fast convergence rates and small midadjustment errors and (2) in nonstationary environments, the algorithms tend to adjust the step sizes so as to give close to the best possible performance. Several simulation examples demonstrating the good properties of the adaptive filters are also presented.>

Consideration is given to a class of suboptimum detectors for data transmitted asynchronously by K users employing direct-sequence spread-spectrum multiple access (DS/SSMA) on the additive white Gaussian noise (AWGN) channel. The general structure of these detectors consists of a bank of matched filters, a linear transformation that operates on the matched-filter outputs, and a set of threshold devices. The linear transformations are chosen to minimize either a mean-squared-error or a weighted-squared-error performance criterion. Each detector can be implemented using a tapped delay line. The number of computations performed per detected bit is linear in K in each case, and the resulting detectors are thus much simpler than the optimum detector. Under typical operating conditions, these detectors will perform much better than the conventional receiver and often nearly as well as the optimum detector.>

The application of sequential decoding to the detection of data transmitted over the additive white Gaussian noise channel by K asynchronous transmitters using direct-sequence spread-spectrum multiple access (DS/SSMA) is considered. A modification of R.M. Fano's (1963) sequential-decoding metric, allowing the messages from a given user to be safely decoded if its E/sub b//N/sub 0/ exceeds -1.6 dB, is presented. Computer simulation is used to evaluate the performance of a sequential decoder that uses this metric in conjunction with the stack algorithm. In many circumstances, the sequential decoder achieves results comparable to those obtained using the much more complicated optimal receiver.>

The authors present a novel technique for image vector quantization (VQ) at a bit rate of 0.75 bits/pixel or below with moderately low to very low subjective distortion. The encoding scheme incorporates the multiplicative visual model as part of the distortion measure. By modeling the quantization noise as an additive signal-dependent noise process, an optimal pre- and postprocessing system, which minimizes the mean-squared error measured inside the visual model, is derived. The design algorithm for this optimal system is also developed. Using this optimal system, a set of experiments was conducted. Images of surprising quality have been produced at the bit rate of 0.1 bits/pixel with a compression rate of 80:1 relative to a normal 8 bits/pixel original.>

A novel visual model of brightness perception is introduced to resolve apparent incompatibilities among Fechner's logarithmic relation, Steven's power law response, and psychophysical and electrophysiological K/sub m/*I/(I+S) experimental results. The proposed visual model, which is based on combining and extending two existing ones (the multiplicative and photoreceptor visual models), is shown to predict these results successfully. Thus a unification of three apparently conflicting visual laws emerges, and the versatility and validity of the visual model are demonstrated.>