nils-herrmann/clean_scirepeval huggingface dataset:
An algorithm based on the even moments of the error

We propose an algorithm based on a linear combination of the even moments of the error for adaptive filtering, called weighted even moment (WEM) algorithm. It is similar to the well-known least mean square (LMS) and to the family of algorithms proposed by Walach and Widrow (1994). This later ones were shown to behave poorer than the LMS, however, when the noise was Gaussian. We study the WEM algorithm convergence behavior and deduce equations for the misadjustment and the learning time. The results showed that the WEM had better performance than the LMS when the noise had a Gaussian distribution.
A fast, on-line algorithm for PCA and its convergence characteristics

Eigendecompositions play a very important role in a variety of signal processing applications. We derive and study an algorithm for principal component analysis (PCA) which is both online and fast converging and which has been presented earlier as a heuristic alternative to the power method. A rule to extract the maximum eigencomponent is first presented, and then online deflation is applied to estimate the minor components. The algorithm is compared with the traditional Sanger's rule through simulations. The convergence properties of the algorithm are explored thoroughly and we present a complete proof explaining the behavior of the algorithm.
Decoding hand trajectories from ECoG recordings via kernel least-mean-square algorithm

Prediction of two dimensional hand trajectories from cortical surface recordings entails finding a functional mapping from spectral modulations in multidimensional channels to instantaneous hand positions. Such studies thus far have been conducted through linear adaptive filters, even though, the functional mapping from the cortical activity to behavior might be nonlinear. Herein, we employ a nonlinear adaptive filter, kernel least mean square (KLMS), which nonlinearly map inputs to a higher dimensional feature space in which inner products can be efficiently computed. The methodology is a simple and effective nonlinear extension of the least mean square (LMS) algorithm. Preliminary results show significant improvements in mean squared error (MSE) values of reconstructed trajectories compared to linear methods (LMS) at a confidence level of 95% in the axis of highest excursion.
Temporal decorrelation using teacher forcing anti-Hebbian learning and its application in adaptive blind source separation

This paper proposes a network architecture to compute on-line the temporal crosscorrelation function between two signals, either stationary or locally stationary. We show that the weights of a multi-FIR (finite impulse response) filter trained with a teacher forcing anti-Hebbian rule encode the crosscorrelation function between the input and the desired response. We extend this network to the Gamma filter which is an IIR (infinite impulse response) filter and also to nonlinear filters. This temporal correlation idea is applied to the blind source separation problem. From these networks we build a recurrent system trained on-line with anti-Hebbian learning which performs temporal decorrelation on the mixed signals. The system performance is tested in speech signals mixed in time with good results. A comparison of the performance among the different topologies is also presented in the paper.
Quantification of inter-trial non-stationarity in spike trains from periodically stimulated neural cultures

In neuroscience, non-stationarity detection of spike trains is useful for ensuring stability of experimental condition, and detecting plasticity. A novel method for estimating point process divergence and its application for non-stationarity detection in spike trains is proposed. The method for measuring divergence is based on decomposition of finite point process and Hilbertian metrics. The method is demonstrated by detecting short-term and long-term plasticity in neural culture probed with periodic stimulations.
Support vector machines for SAR automatic target recognition

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. A real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) is presented and the result is compared with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local "bounded" decision region around each class that presents better rejection to confusers.
Accurate linear parameter estimation in colored noise

Estimation of the parameters of an unknown system is an important problem in signal processing. The classical mean squared error (MSE) criterion and its variants have been widely used to solve this problem. However, it is well known that the MSE criterion produces biased parameter estimates when the signals of interest (especially the input) are corrupted with additive noise having arbitrary or no coloring (white). Alternative approaches require additional system constraints and explicit estimation of the noise covariances. Recently, we proposed a new criterion called the error whitening criterion (EWC) along with associated algorithms that solved the problem when the additive disturbances are white. However, the performance of EWC is not satisfactory when the disturbances are correlated (colored). In this paper, we propose a method based on the principles of the EWC that can consistently estimate the parameters of an unknown arbitrary linear system in colored input noise without estimating the noise covariances. We then present a novel stochastic gradient algorithm that estimates the optimal parameters in an on-line fashion. We briefly discuss the convergence of this algorithm and present extensive simulation results to show the superiority of this criterion over MSE.
Modified Freeman model: a stability analysis and application to pattern recognition

The biologically realistic Freeman model of the olfactory cortex has been used to solve some engineering problems. However, due to the nature of the nonlinear function in the model, only numerical computer simulations can help explore the behavior of the system for different sets of control parameters. We modify the nonlinear function with a piecewise linear model and show that this simplified model exhibits the same qualitative behavior as the original one. Moreover, for this modified model, we employ the analytical tools of nonlinear dynamics to understand the system response for different parameter values. Finally, similar to the original system, we show that the modified system can be used as an auto-associative memory.
Analysis of Biomedical Signals by the Lempel-Ziv Complexity: the Effect of Finite Data Size

The Lempel-Ziv (LZ) complexity and its variants are popular metrics for characterizing biological signals. Proper interpretation of such analyses, however, has not been thoroughly addressed. In this letter, we study the the effect of finite data size. We derive analytic expressions for the LZ complexity for regular and random sequences, and employ them to develop a normalization scheme. To gain further understanding, we compare the LZ complexity with the correlation entropy from chaos theory in the context of epileptic seizure detection from EEG data, and discuss advantages of the normalized LZ complexity over the correlation entropy
From compressive to adaptive sampling of neural and ECG recordings

The miniaturization required for interfacing with the brain demands new methods of transforming neuron responses (spikes) into digital representations. The sparse nature of neural recordings is evident when represented in a shift invariant basis. Although a compressive sensing (CS) framework may seem suitable in reducing the data rates, we show that the time varying sparsity in the signals makes it difficult to apply. Furthermore, we present an adaptive sampling scheme which takes advantage of the local characteristics of the neural spike trains and electrocardiograms (ECG). In contrast to the global constraints imposed in CS our solution is sensitive to the local time structure of the input. The simplicity in the design of the integrate-and-fire (IF) make it a viable solution in current brain machine interfaces (BMI) and ambulatory cardiac monitoring.
Temporal plasticity in self-organizing networks

We propose a principle that adds temporal plasticity to self-organizing networks. The algorithm uses activity diffusion to couple space and time into a single set of dynamics that can help disambiguate the static spatial information with temporal information. The approach has been successfully applied to the neural gas algorithm. We present a simple temporal example which illustrates the fundamentals of the network as well as comparing the results of our approach vs. the neural gas algorithm as applied to time-series prediction of a chaotic signal.
Comments on "Sinc interpolation of discrete periodic signals"

In a recent paper by T. Schanze (see ibid., vol.43, p.1502-3, 1995) the convolution of the sinc kernel with the infinite sequence of a periodic function was expressed as a finite summation. The expression obtained, however, is not numerically stable when evaluated at or near integer values of time. This correspondence presents a numerically stable formulation that is equivalent to the results reported in the above-cited paper and that, when sampled, is also shown to be equivalent to the inverse discrete Fourier transform (IDFT).
Signal Processing with Echo State Networks in the Complex Domain

Signal processing in the complex domain is an essential part of signal processing particularly in digital communication systems. Considerable efforts have been made to convert the well established tools of real signal processing such as backpropagation training algorithm for multilayer perceptron to perform in complex domain. In this paper, we propose the echo state network (ESN) approach for complex domain signal processing. The complex ESN (CESN) replaces the real connection weights for the reservoir and readout with complex numbers and the real activation functions with fully complex nonlinearities. CESNs provide much faster and simpler learning compared to other techniques in the literature since ESN does not backpropagate the errors through complex nonlinearities for training but only adapts a feedforward linear readout. Experiment on nonlinear channel equalization show superiority of CESN in terms of lower symbol error rates in addition to the fast and simple training.
Multiresolution using principal component analysis

This paper proposes principal component analysis (PCA) to find adaptive bases for multiresolution. An input image is decomposed into components (compressed images) which are uncorrelated and have maximum l/sub 2/ energy. With only minor modification, a single layer linear network using the generalized Hebbian algorithm (GHA) is used for multiresolution PCA. The decomposition has been successfully applied to face classification. Good results with biological signals have also been reported.
Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control

The technique of local linear models is appealing for modeling complex time series due to the weak assumptions required and its intrinsic simplicity. Here, instead of deriving the local models from the data, we propose to estimate them directly from the weights of a self-organizing map (SOM), which functions as a dynamic preserving model of the dynamics. We introduce one modification to the Kohonen learning to ensure good representation of the dynamics and use weighted least squares to ensure continuity among the local models. The proposed scheme is tested using synthetic chaotic time series and real-world data. The practicality of the method is illustrated in the identification and control of the NASA Langley wind tunnel during aerodynamic tests of model aircraft. Modeling the dynamics with an SOM lends to a predictive multiple model control strategy. Comparison of the new controller against the existing controller in test runs shows the superiority of our method.
Information-theoretic metric learning: 2-D linear projections of neural data for visualization

Intracortical neural recordings are typically high-dimensional due to many electrodes, channels, or units and high sampling rates, making it very difficult to visually inspect differences among responses to various conditions. By representing the neural response in a low-dimensional space, a researcher can visually evaluate the amount of information the response carries about the conditions. We consider a linear projection to 2-D space that also parametrizes a metric between neural responses. The projection, and corresponding metric, should preserve class-relevant information pertaining to different behavior or stimuli. We find the projection as a solution to the information-theoretic optimization problem of maximizing the information between the projected data and the class labels. The method is applied to two datasets using different types of neural responses: motor cortex neuronal firing rates of a macaque during a center-out reaching task, and local field potentials in the somatosensory cortex of a rat during tactile stimulation of the forepaw. In both cases, projected data points preserve the natural topology of targets or peripheral touch sites. Using the learned metric on the neural responses increases the nearest-neighbor classification rate versus the original data; thus, the metric is tuned to distinguish among the conditions.
Analysis ECoG features for movement execution using denoising source separation

The major challenge in ECoG-based neuroprosthesis is isolating features in a spectrally and spatially broad range of sources essential for modeling motor behavior. In this study, movement-related spectral modulations are resolved using broadband ECoG recordings passed through a filterbank of constant-Q filters. Denoising source separation is a semiblind source separation methodology which extracts hidden structures of interest within the data by exploiting prior knowledge on the observations. Herein, the methodology is utilized to extract sources that modulate within the frequency content of the hand trajectory. High signal acquisition rates (12 kHz) allow for analysis of frequencies beyond the fast gamma oscillations which have been thus far discarded as background activity. Exploratory analysis suggests the first components extracted from envelopes of high spectral bands correlate with the hand trajectory and their spatial distribution covers areas of premotor and primary motor cortices.
Brain-Machine Interface Control via Reinforcement Learning

We investigate the capabilities of reinforcement learning (RL) to create a brain-machine interface (BMI) that uses Q(lambda) learning to find the functional mapping between neural activity and intended behavior. This paradigm shift is intended to address the issue of paralyzed and amputee patients whom are physically unable to move, which is necessary to train traditional supervised learning BMIs. We created a RLBMI architecture incorporating a rat behavioral paradigm for prosthetic arm control. The performance results show 'proof of concept' that RLBMI can learn the temporal structure of neural signals to control a prosthetic arm
Spatio-temporal clustering of firing rates for neural state estimation

Characterizing the dynamics of neural data by a discrete state variable is desirable in experimental analysis and brain-machine interfaces. Previous successes have used dynamical modeling including Hidden Markov Models, but the methods do not always produce meaningful results without being carefully trained or initialized. We propose unsupervised clustering in the spatio-temporal space of neural data using time embedding and a corresponding distance measure. By defining performance measures, the method parameters are investigated for a set of neural and simulated data with promising results. Our investigations demonstrate a different view of how to extract information to maximize the utility of state estimation.
Binary classification based on SVDD projection and nearest neighbors

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. The usual strategy of the SVDD depends on the process of finding the region for the normal-class training data with somewhat neglecting the distribution of the abnormal data, thus it may not work well if applied to the binary classification problems in which two classes have similar number of data. In this paper, we consider the problem of performing binary classification based on the SVDD techniques, and in order to overcome the possible drawback of the usual SVDD strategy focusing on the normal-class data only, we propose a new SVDD algorithm which is based on the use of two different SVDD balls for the positive and negative classes along with the SVDD projection and nearest neighbor rule. To investigate how the proposed method works, we compared the performance of the proposed method with SVC (support vector classifier) and conventional SVDD using several real datasets.
The gamma-filter-a new class of adaptive IIR filters with restricted feedback

Generalized feedforward filters, a class of adaptive filters that combines attractive properties of finite impulse response (FIR) filters with some of the power of infinite impulse response (IIR) filters, are described. A particular case, the gamma filter, generalizes Widrow's adaptive transversal filter (adaline) to an infinite impulse response filter. Yet, the stability condition for the gamma filter is trivial, and LMS adaptation is of the same computational complexity as the conventional transversal filter structure. Preliminary results indicate that the gamma filter is more efficient than the adaptive transversal filter. The authors extend the Wiener-Kopf equation to the gamma filter and develop some analysis tools.
Generalized Correntropy Matching Pursuit: A novel, robust algorithm for sparse decomposition

We introduce a novel variation on the well-known Matching Pursuit (MP) algorithm. In particular, the sparse approximation problem is solved in a greedy scheme using estimated higher-order statistics as similarity measures instead of the somehow limited second-order statistics that perform optimally only under Gaussian assumptions. This is conveyed via the generalized correntropy (GC) function instead of the cross-correlation approach usually utilized in stochastic random processes applications. Additionally, extra flexibility is achieved by the GC parameters that control the behavior of the induced metric. The result is the robust Generalized Correntropy Matching Pursuit (GCMP) algorithm. Furthermore, we present results on two different frameworks dealing with detection and sparse approximation and highlight the robustness of this method in the presence of high-tailed impulsive noise.
Learning from examples with quadratic mutual information

Discusses an algorithm to train nonlinear mappers with information theoretic criteria (entropy or mutual information) directly from a training set. The method is based on a Parzen window estimator and uses Renyi's quadratic definition of entropy and a distance measure based on the Cauchy-Schwartz inequality. We apply the algorithm to the difficult problem of vehicle pose estimation in synthetic aperture radar (SAR) with very good results.
Robust bounded logistic regression in the class imbalance problem

In this paper, we propose to deal with the problems of logistic regression with outliers and class imbalance, which are common in a wide range of practical applications. The robust bounded logistic regression with different error costs is developed to reduce the combined influence of outliers and class imbalance. First, inspired by the Correntropy induced loss function, we develop the bounded logistic loss function which is a monotonic, bounded and nonconvex loss and thus robust to outliers. With the bounded logistic loss, we construct a new robust logistic regression. Second, under the principle of cost-sensitive learning, we assign different error costs for different classes in order to reduce the sensitiveness of the new robust logistic regression to class imbalance. Using the half-quadratic optimization method, it is easy to optimize the proposed logistic regression model. Experimental results demonstrate that our proposed method improves the performance of logistic regression on the datasets with outliers and class imbalance.
Modulation Selection from a Battery Power Efficiency Perspective: A Case Study of PPM and OOK

In this paper, we compare the battery power efficiencies of various pulse-based modulations widely adopted for their low complexity. Taking into account circuit modules and battery imperfectness, we establish simple closed-form analytical formulas which can be used to conveniently determine the relative preference between arbitrary pulse-based modulation pairs in terms of their actual average battery energy consumption.
A Fixed-Point Minimum Error Entropy Algorithm

In this paper, we propose the fixed-point minimum error entropy (fixed-point MEE) as an alternative to the minimum error entropy (MEE) algorithm for training adaptive systems. The fixed-point algorithms are different from the gradient methods like MEE, and are proven to be faster, more stable and step-size free. This characteristic is due to the second order update similar to recursive least- squares (RLS) that tracks the Wiener solution with every update. We study the effect of design parameters, namely the forgetting factor, the window length, and the kernel size, on the convergence properties of the newly introduced recursive Fixed-Point MEE. Also, we test the performance of both the algorithms for two classic problems of system identification. Finally, we conclude that the Fixed-Point MEE performs better than MEE.
Detection of synchronized firings in multivariate neural spike trains during motor tasks

This paper describes and compares two classical methods for the detection of neuron groups which exhibit synchronized firings in multivariate spike trains. These methods were compared on experimental and randomized data corresponding to the firing activity of 104 neurons located in motor, premotor, and parietal cortices in a monkey during movement tasks. Both methods exhibited high false positive rates in randomized data, but results showed that this rate can be advantageously reduced with a simple postprocessing. Otherwise, one method permitted to detect a significant number of synchronized groups of neurons related to the behavioral task.
Classification with linear networks using an online constrained LDA algorithm

Principal component analysis (PCA) and linear discriminant analysis (LDA) are two statistical tools utilized in many signal processing areas such as data compression and pattern recognition. Online algorithms, such as Oja's rule, have found wide application for PCA and it has been generalized in our previous paper (1997) to LDA (Fisher criterion) using the framework of gradient descent learning. In this paper, the rule is further extended to accept the case of constraints in the feature extraction stage as is often necessary for real world applications. As examples, the new rule is applied to regularizers in the form of both a 2D Gaussian filter for handwritten digit classification and determination of the memory depth of the Gamma filter for isolated word recognition. Results show the good behavior of the learning rule and the advantage of using regularization for improved generalization.