nils-herrmann/clean_scirepeval huggingface dataset:
Differential EEG

We propose to improve the spatial resolution of electroencephalography (EEG) using a differential recording methodology. Conventional EEG (CEEG) systems independently amplify and digitize the signal from each electrode. The Differential EEG (DEEG) approach amplifies the minute difference signal between neighboring electrodes which greatly eases the burden on the subsequent amplification and data conversion stages. A custom DEEG system was designed and fabricated in a 0.6 um CMOS process. The fabricated chip was built into a small prototype DEEG recording system used to measure alpha waves from the occipital lobe of a human subject. We show that the differential approach suffers from fewer artifacts in an eye open/closed determination task than a commercial CEEG system. Artifacts are mainly due to the signal level dependent noise and amplifier mismatch in the CEEG amplifiers. All of our experiments were run with a fixed 3 cm electrode spacing, however we expect to improve the resolution of EEG below 1 cm electrode spacing.
A Novel Weighted LBG Algorithm for Neural Spike Compression

In this paper, we present a weighted Linde-Buzo-Gray algorithm (WLBG) as a powerful and efficient technique for compressing neural spike data. We compare this technique with the recently proposed self-organizing map with dynamic learning (SOM-DL) and the traditional SOM. A significant achievement of WLBG over SOM-DL is a 15 dB increase in the SNR of the spike data apart from having a compression ratio of 150 : 1. Being simple and extremely fast, this algorithm allows real-time implementation on DSP chips opening new opportunities in BMI applications.
Hierarchical clustering of neural data using Linked-Mixtures of Hidden Markov Models for Brain Machine Interfaces

In this paper, we build upon previous Brain Machine Interface (BMI) signal processing models that require a-priori knowledge about the patient's arm kinematics. Specifically, we propose an unsupervised hierarchical clustering model that attempts to discover both the interdependencies between neural channels and the self-organized clusters represented in the spatial-temporal neural data. Given that BMIs must work with disabled patients who lack arm kinematic information, the clustering work describe within this paper is very relevant for future BMIs.
Cooperative Diversity of Spectrum Sensing for Cognitive Radio Systems

Spectrum sensing is a crucial issue in cognitive radio networks. To improve the sensing performance, cooperation among the secondary users can be utilized to collect space diversity. In this paper, we quantify the diversity order for various cooperative spectrum sensing strategies. We first notice that, in a spectrum sensing problem, diversity should reflect both the false alarm and missed detection behaviors since they respectively capture the efficiency and reliability of the overall cognitive system. Hence, we consider both false alarm and missed detection probabilities individually and jointly via the average error probability. With the knowledge of both the noise strength and the primary user signal strength, the threshold of the Neyman-Pearson detector can be varied to control the system performance in desirable ways. Interesting tradeoffs between system efficiency and reliability are found in various cooperative strategies and analytical results are presented to guide practical system designs with different preferences.
Coadaptive Brain–Machine Interface via Reinforcement Learning

This paper introduces and demonstrates a novel brain-machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with the environment, rather than an explicit training signal. Coadaption enables continuous, synergistic adaptation between the BMI control algorithm and BMI user working in changing environments. Shaping is designed to reduce the learning curve for BMI users attempting to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the user's neuronal activity. This semisupervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty. All three subjects coadapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.
Special Issue on Advances in Kernel-Based Learning for Signal Processing [From the Guest Editors]

he importance of learning and adaptation in statistical signal processing creates a ymbiotic relationship with machine learning. However, the two disciplines possess different momentum and emphasis, which makes it attractive to periodically review trends and new developments in their overlapping spheres of influence. Looking at the recent trends in machine learning, we see increasing interest in kernel methods, Bayesian reasoning, causality, information theoretic learning, reinforcement learning, and nonnumeric data processing, just to name a few. While some of the machine-learning community trends are clearly visible in signal processing, such as the increased popularity of the Bayesian methods and graphical models, others such as kernel approaches are still less prominent. However, kernel methods offer a number of unique advantages for signal processing, and this special issue aims to review some of those.
An Improved Minimum Error Entropy Criterion with Self Adjusting Step-Size

In this paper, we propose minimum error entropy with self adjusting step-size (MEE-SAS) as an alternative to the minimum error entropy (MEE) algorithm for training adaptive systems. MEE-SAS has faster speed of convergence as compared to MEE technique for the same misadjustment. We attribute this characteristic to automatic learning rate inherent in MEE-SAS where the changing step size helps the algorithm to take large "jumps" when far away from the optimal solution and small "jumps" when near the solution. We test the performance of both the algorithms for two classic problems of system identification and prediction. However, we show that MEE performs better than MEE-SAS in situations where tracking ability of the optimal solution is required like in the case of non-stationary signals
A scalable RC architecture for mean-shift clustering

The mean-shift algorithm provides a unique non-parametric and unsupervised clustering solution to image segmentation and has a proven record of very good performance for a wide variety of input images. It is essential to image processing because it provides the initial and vital steps to numerous object recognition and tracking applications. However, image segmentation using mean-shift clustering is widely recognized as one of the most compute-intensive tasks in image processing, and suffers from poor scalability with respect to the image size (N pixels) and number of iterations (k): O(kN2). Our novel approach focuses on creating a scalable hardware architecture fine-tuned to the computational requirements of the mean-shift clustering algorithm. By efficiently parallelizing and mapping the algorithm to reconfigurable hardware, we can effectively cluster hundreds of pixels independently. Each pixel can benefit from its own dedicated pipeline and can move independently of all other pixels towards its respective cluster. By using our mean-shift FPGA architecture, we achieve a speedup of three orders of magnitude with respect to our software baseline.
Maximum Correntropy Estimation Is a Smoothed MAP Estimation

As a new measure of similarity, the correntropy can be used as an objective function for many applications. In this letter, we study Bayesian estimation under maximum correntropy (MC) criterion. We show that the MC estimation is, in essence, a smoothed maximum a posteriori (MAP) estimation, including the MAP and the minimum mean square error (MMSE) estimation as the extreme cases. We also prove that under a certain condition, when the kernel size in correntropy is larger than some value, the MC estimation will have a unique optimal solution lying in a strictly concave region of the smoothed posterior distribution.
Correntropy kernel temporal differences for reinforcement learning brain machine interfaces

This paper introduces a novel temporal difference algorithm to estimate a value function in reinforcement learning. This is a kernel adaptive system using a robust cost function called correntropy. We call this system correntropy kernel temporal differences (CKTD). This algorithm is integrated with Q-learning to find a proper policy (Q-learning via correntropy kernel temporal differences). The proposed method was tested with a synthetic problem, and its robustness under a changing policy was quantified. The same algorithm was applied to the decoding of a monkey's neural states in a reinforcement learning brain machine interface (RLBMI) in a center-out reaching task. The results showed the potential advantage of the proposed algorithm in the RLBMI framework.
Improving ATR performance by incorporating virtual negative examples

One common problem in learning from examples is the insufficient size of the training set. Many researchers have proposed methods to counteract this shortcoming, such as the noisy interpolation theory, hints, new distance measure (tangent distance), virtual examples, etc. This paper presents the idea of creating virtual negative examples as severe distortions of the known class patterns. Two classifiers are studied, a perceptron and a support vector machine trained to recognize objects in synthetic aperture radar (SAR) images. They utilize the training set (positive examples) to create the discriminant function of each class in the conventional way. On the other hand, the virtual negative examples will help determine the regions where the discriminant function should yield a low value. The experimental results show that incorporating the negative examples improves greatly (up to 50 percent improvement) the confuser rejection rates.
An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems

The paper investigates error-entropy-minimization in adaptive systems training. We prove the equivalence between minimization of error's Renyi (1970) entropy of order /spl alpha/ and minimization of a Csiszar (1981) distance measure between the densities of desired and system outputs. A nonparametric estimator for Renyi's entropy is presented, and it is shown that the global minimum of this estimator is the same as the actual entropy. The performance of the error-entropy-minimization criterion is compared with mean-square-error-minimization in the short-term prediction of a chaotic time series and in nonlinear system identification.
Tracking spectral resonances

An adaptive filter approach that tracks the time-varying parameters of the vocal tract and updates the parameters during the glottal closed phase interval can reduce the formant estimation error. The authors describe three adaptive algorithms for estimating time-varying vocal track parameters: weighted recursive least squared (WRLS), WRLS with variable forgetting factor (WRLS-VFF), and weighted least squared lattice (WLSL). A recursive equation to update the forgetting factor in the WRLS-VFF algorithm is derived, and experimental results for both synthetic and real speech for the above algorithms are given. Results show that the WRLS-VFF algorithm offers a more accurate formant estimation and a faster formant tracking ability than either the WLSL or the WRLS algorithm.<<ETX>>
Pitch estimation using non-negative matrix factorization

The problem of pitch detection consists of estimating the dominant frequency present in a certain time window. This paper demonstrates and analyzes the use of a non-negative matrix factorization technique with a frequency basis formed with a correntropy kernel. This offers the advantage that the frequency basis is adaptable, allowing the matrix factorization to fit the data precisely, as well as including a dictionary specifically to account for noise. Using non-negative matrix factorization also allows an increase in dimensionality, which increases the frequency resolution of the algorithm. The method is tested on a database of trumpet notes and compared to other current methods, improving on their performance for noisy signals.
Crosscorrelation estimation using teacher forcing Hebbian learning and its application

This paper proposes a new network architecture to compute the temporal crosscorrelation function between two signals, either stationary or local stationary. We show that the weights of a multi-FIR-like filter trained with a teacher forcing Hebbian rule encode the crosscorrelation function between the input and the desired response. This temporal correlation idea is applied to the blind sources separation problem. And experimental results are also given to show the validation of the idea.
Projentropy: Using entropy to optimize spatial projections

Methods for hypothesis testing on zero-mean vector-valued signals often rely on a Gaussian assumption, where the second-order statistics of the observed sample are sufficient statistics of the conditional distribution. This yields fast and simple tests, but by using information-theoretic statistics one can relax the Gaussian assumption. We propose using Rényi's quadratic entropy as an alternative to the covariance and show how a linear projection can be optimized to maximize the difference between the conditional entropies. In addition, if the observed sample is actually a window of a multivariate time-series, then the temporal structure can be exploited using the generalized auto-correlation function, correntropy, of the projected sample. This both reduces the computational complexity and increases the performance. These tests can be applied for decoding the brain state from electroencephalogram (EEG) recordings. Preliminary results are demonstrated on a brain-computer interface competition dataset. On unfiltered signals, the projections optimized with the entropy-based statistic perform better than those of common spatial pattern (CSP) algorithm in terms of classification performance.
A Recursive Online Kernel PCA Algorithm

In this paper, we describe a new method for performing kernel principal component analysis which is online and also has a fast convergence rate. The method follows the Rayleigh quotient to obtain a fixed point update rule to extract the leading eigenvalue and eigenvector. Online deflation is used to estimate the remaining components. These operations are performed in reproducing kernel Hilbert space (RKHS) with linear order memory and computation complexity. The derivation of the method and several applications are presented.
Echo cancellation with the gamma filter

Compares the Gamma filter with other feedback and feedforward structures in the problem of echo cancellation for digital data communication in the subsciber loop. The authors show that the IIR structures perform better than the corresponding FIR structures, and that the Gamma filter adapts faster than the cascade structure. They also show how the Gamma filter can be adapted using RLS for the filter weights and block LMS for the recursive parameter.<<ETX>>
The wellposedness analysis of the kernel adaline

In this paper, we investigate the wellposedness of the kernel adaline. The kernel adaline finds the linear coefficients in a radial basis function network using deterministic gradient descent. We will show that the gradient descent provides an inherent regularization as long as the training is properly early-stopped. Along with other popular regularization techniques, this result is investigated in a unifying regularization-function concept. This understanding provides an alternative and possibly simpler way to obtain regularized solutions comparing with the cross-validation approach in regularization networks.
Learning Nonlinear Generative Models of Time Series With a Kalman Filter in RKHS

This paper presents a novel generative model for time series based on the Kalman filter algorithm in a reproducing kernel Hilbert space (RKHS) using the conditional embedding operator. The end result is a nonlinear model that quantifies the hidden state uncertainty and propagates its probability distribution forward as in the Kalman algorithm. The embedded dynamics can be described by the estimated conditional embedding operator constructed directly from the training measurement data. Using this operator as the counterpart of the state transition matrix, we reformulate the Kalman filter algorithm in RKHS. For the state model, the hidden states are the estimated embeddings of the measurement distribution, while the measurement model serves to connect the estimated measurement embeddings with the current mapped measurements in the RKHS. This novel algorithm is applied to noisy time-series estimation and prediction, and simulation results show that it outperforms other existing algorithms. In addition, improvements are proposed to reduce the size of the operator and reduce the computation complexity.
Backpropagation through time with fixed memory size requirements

A generalization of the backpropagation through time (BPTT) algorithm is presented, which, under reasonable assumptions, can lead to fixed memory size requirements. The idea is to model BPTT as the storage of activations and errors in tapped delay lines, and then generalize the tap delay line to a gamma memory. Since the depth of the gamma filter is T=K//spl mu/, where K is the filter order and /spl mu/ a scalar that can be varied between 0</spl mu/<1, it is possible to achieve depth T with a fixed filter order K. The accuracy of the gradient computation is tested in an example.<<ETX>>
Temporal context in object recognition

Sparse coding has become a popular way to learn feature representation from the data itself. However, temporal context, when present, can provide useful information and alleviate instability in sparse representation. Here we show that when sparse coding is used in conjunction with a dynamical system, the extracted features can provide better descriptors for time-varying observations. We show a marked improvement in classification performance on COIL-100 and animal datasets using our model. We also propose a simple extension to our model to learn invariant representations.
Robust single-trial ERP estimation based on spatiotemporal filtering

Most spatiotemporal filtering methods for the problem of single-trial event-related potentials (ERP) estimation relies on the analysis of the second-order statistics (SOS) of electroencephalograph (EEG) data. Due to the noisy nature of EEG, these methods often suffer from the outliers in EEG. We combine a recently proposed spatiotemporal filtering method with the maximum correntropy criterion (MCC) for the single-trial estimation of the ERP amplitude. Study with real cognitive ERP data shows the robustness of the method with reduced estimation variance.
Non-linear time series modeling with self-organization feature maps

A locally linear approach based on Kohonen self-organizing feature mapping (SOFM) is proposed for the modeling of nonlinear time series. This approach exploits the neighborhood preserving property of Kohonen feature maps. The key difference is that the local model fitting is performed directly over a matched neighborhood of the constructed SOFM neural field. The initial results show that this neural network scenario is an effective approach for local modeling of low dimensional nonlinear processes.
Spectral feature extraction using Poisson moments

We propose to use the Gamma filter as a feature extractor for the preprocessing of speech signals. Gamma filter which can be implemented as a cascade of identical first order lowpass filters generates at its taps the Poisson moments of an input signal. These moments carry spectral information about the recent history of the input signal. They can be used to construct time-frequency representations as an alternative to the conventional methods of short term Fourier transform, cepstrum, etc. In this study it is shown that when the time scale of the Gamma filter is chosen properly, the Poisson moments correspond to the Taylor's series expansion coefficients of the input signal spectra. The appeal of the proposed method comes from the fact that in the analog domain the moments are available as a continuous time electrical signal and can be physically measured, rather than computed off-line by a digital computer. With this convenience, the speed of the discrete time processor following the preprocessor is independent of the highest frequency of the input signal, but is constrained with the stationarity duration of the signal.<<ETX>>
Hierarchal Decomposition of Neural Data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI

In this paper, we propose a simple algorithm that takes multidimensional neural input data and decomposes the joint likelihood into marginals using boosted mixtures of hidden Markov chains (BM-HMM). The algorithm applies techniques from boosting to create hierarchal dependencies between these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is weighted and incorporated into an ensemble decision. Our results show that this algorithm is very simple to train and computationally efficient, while also providing the ability to reduce the input dimensionality for brain machine interfaces (BMIs).
Error whitening criterion for adaptive filtering: theory and algorithms

Mean squared error (MSE) has been the dominant criterion in adaptive filter theory. A major drawback of the MSE criterion in linear filter adaptation is the parameter bias in the Wiener solution when the input data are contaminated with noise. We propose and analyze a new augmented MSE criterion called the Error Whitening Criterion (EWC). EWC is able to eliminate this bias when the noise is white. We will determine the analytical solution of the EWC, discuss some interesting properties, and develop stochastic gradient and other fast algorithms to calculate the EWC solution in an online fashion. The stochastic algorithms are locally computable and have structures and complexities similar to their MSE-based counterparts (LMS and NLMS). Convergence of the stochastic gradient algorithm is established with mild assumptions, and upper bounds on the step sizes are deduced for guaranteed convergence. We will briefly discuss an RLS-like Recursive Error Whitening (REW) algorithm and a minor components analysis (MCA) based EWC-total least squares (TLS) algorithm and further draw parallels between the REW algorithm and the Instrumental Variables (IV) method for system identification. Finally, we will demonstrate the noise-rejection capability of the EWC by comparing the performance with MSE criterion and TLS.
A Pulse-Based Feature Extractor for Spike Sorting Neural Signals

Spike sorting is often required for analyzing neural recordings to isolate the activity of single neurons. Wave shape analysis in the spike sorting procedure provides a means to detect spikes while minimizing the influence of false alarms. As neural recording techniques allow for recording hundreds of electrodes, power is too limited in neural implants for current spike sorting algorithms. Even with spike sorting at the back-end where more power is available, the bandwidth is too limited to transmit enough information for current spike sorting techniques. A low-power pulse-based feature extractor presented in the paper is a solution to the bandwidth bottleneck. It reduces the bandwidth of the neural signal by several orders of magnitude while preserving enough information for spike sorting