nils-herrmann/clean_scirepeval huggingface dataset:
Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces

Many decoding algorithms for brain machine interfaces' (BMIs) estimate hand movement from binned spike rates, which do not fully exploit the resolution contained in spike timing and may exclude rich neural dynamics from the modeling. More recently, an adaptive filtering method based on a Bayesian approach to reconstruct the neural state from the observed spike times has been proposed. However, it assumes and propagates a gaussian distributed state posterior density, which in general is too restrictive. We have also proposed a sequential Monte Carlo estimation methodology to reconstruct the kinematic states directly from the multichannel spike trains. This letter presents a systematic testing of this algorithm in a simulated neural spike train decoding experiment and then in BMI data. Compared to a point-process adaptive filtering algorithm with a linear observation model and a gaussian approximation (the counterpart for point processes of the Kalman filter), our sequential Monte Carlo estimation methodology exploits a detailed encoding model (tuning function) derived for each neuron from training data. However, this added complexity is translated into higher performance with real data. To deal with the intrinsic spike randomness in online modeling, several synthetic spike trains are generated from the intensity function estimated from the neurons and utilized as extra model inputs in an attempt to decrease the variance in the kinematic predictions. The performance of the sequential Monte Carlo estimation methodology augmented with this synthetic spike input provides improved reconstruction, which raises interesting questions and helps explain the overall modeling requirements better.
A neural network development environment for adaptive inverse control

Presents a neurocontroller development environment using the ideas of adaptive inverse control. The goal of the paper is to describe the environment utilized and show a very simple example. A PC running a commercial simulator communicates in real-time with a /spl mu/controller to learn how to control the power supply of a motor using the speed as input. This /spl mu/controller runs a feedforward neural network online, but the weights are adapted periodically off-line by the simulator and downloaded once the weights stabilize. The user interacts with the user interface of the commercial package to define the topology, to test the adequacy of the control topology and to set the control parameters.
Tracking the non-stationary neuron tuning by dual Kalman filter for brain machine interfaces decoding

Previous decoding approaches assume stationarity of the functional relationship between the neural activity and animal's movement in brain machine interfaces (BMIs). Studies show that the activity of individual neurons changes considerably from day to day. We propose to implement a dual Kalman structure to track neural tuning during the decoding process. While the kinematics are inferred as the state from the observation of neuron firing rates, the preferred direction of neuron tuning is also optimized by dual Kalman filtering on the linear coefficients of the observation model. When compared with the fixed tuning Kalman filter, the decoding performance of the adaptive dual Kalman filter is better (less Normalized Mean Square Error), which means that the evolving tuning of motor neuron is being tracked.
A Study of Musical Pitch Distance Using a Self-Organized Hierarchical Linear Dynamical System on Acoustic Signals

The hierarchical linear dynamical system (HLDS) is a self-organizing architecture to cluster acoustic time series. The HLDS architecture is equivalent to a Kalman filter whose top-layer state learns to create subspaces that tessellate the acoustic signal in regions that correspond to different musical pitches. The observation layer of the HLDS is built from a biologically plausible gammatone filter bank that provides the representation space for the state assignments. An important characteristic of the methodology is that it is adaptive and self-organizing, i.e., previous exposure to the acoustic input is the only requirement for learning and recognition. In this article we show that the representation space that the algorithm learns from acoustic signals preserves the organization found in monophonic notes, and exhibits (for isolated pitches and triads) properties suggested in the theory of efficient chromatic voice leading and neo-Riemannian theories.
Independent components analysis for fetal electrocardiogram extraction: a case for the data efficient Mermaid algorithm

Fetal heart rate (FHR) monitoring is currently the primary methodology for antenatal determination of fetal well-being. Currently, the FHR can be detected with ultrasonography, but the additional information from fetal electrocardiogram (FECG) is only available via an invasive scalp electrode. A cost effective noninvasive monitoring through standard ECG electrodes could be used on nearly every patient in lieu of the ultrasound monitors. In this method, a number of electrodes are positioned on the abdomen of the mother to collect, simultaneously, various combinations of the signals including the heartbeats of the mother and the fetus. For accurate fetal heart-rate estimation, a clean FECG must be extracted from the collected mixtures. It is well known that this can be achieved using blind source separation (BSS) techniques. In this paper, the performance of the Mermaid algorithm, which is based on minimizing Renyi's mutual information, is evaluated on this problem of great practical importance. The effectiveness and data efficiency of Mermaid and its superiority over alternative information theoretic BSS algorithms are illustrated using artificially mixed ECG signals as well as fetal heart rate estimates in real ECG mixtures.
Analysis of the Correlation between Local Field Potentials and Neuronal Firing Rate in the Motor Cortex

Neuronal firing rate has been the signal of choice for invasive motor brain machine interfaces (BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals
A conditional independence perspective of variable selection

Variable selection is a necessary preprocessing stage in many applications, such as regression and classification, to reduce computational cost, to avoid curse of dimensionality and to improve generalization. A filter type approach to variable selection employs statistical criteria such as dependence to quantify the importance of a variable. In this paper we discuss the use of conditional independence as a criteria for variable selection, and describe a forward selection and a backward elimination based approach using this notion. We introduce two measures of conditional independence, describe their respective estimators and apply them in the variable selection task. We also provide a brief overview of the available variable selection methods and compare the proposed methods with these methods.
Multiple adaptive kernel size KLMS for Beijing PM2.5 prediction

The kernel least mean square (KLMS) algorithm is an efficient non-linear adaptive filter that operates in the reproducing kernel Hilbert space (RKHS). In realistic applications of system identification or time series prediction, there are usually multiple inputs that demand multiple kernels or kernel parameters. This paper proposes to use a tensor product kernel for KLMS that accommodates multiple inputs. Furthermore, instead of arbitrarily setting kernel parameters, appropriate kernel sizes can be chosen by a gradient descent based adaptive algorithm that minimizes the square of instant error, which helps KLMS to better capture the underlying system mechanism. Effectiveness of the proposed algorithm is shown by experiments conducted for both simulated dataset and an important real-world problem - Beijing PM2.5 prediction.
Quantification of neural functional connectivity during an active avoidance task.

Many behavioral and cognitive processes are associated with spatiotemporal dynamic communication between brain areas. Thus, the quantification of functional connectivity with high temporal resolution is highly desirable for capturing in vivo brain function. However, brain functional network quantification from EEG recordings has been commonly used in a qualitative manner. In this paper, we consider pairwise dependence measures as random variables and estimate the pdf for each electrode of the arrangement. A metric imposed by the quadratic Cauchy-Schwartz Mutual Information quantifies these pdfs. We present the results by brain regions simplifying the analysis and visualization drastically. The proposed metric of functional connectivity quantification is addressed for temporal dependencies of the brain network that can be related to the task.
"Trends" Expert Overview Sessions Revived at ICASSP 2011 [In the Spotlight]

M any of the technology trends we see today in audio and acoustic s ignal process ing (AASP) can be traced back to visionary ideas of those we now call luminaries. Key figures in our community include Colin Cherry (1914– 1979) for defining the cocktail party problem; Manfred Schroeder (1926– 2009) for contributions to understanding of acoustics, hearing, music, and speech coding; and many other researchers equally important but too numerous to list. The AASP area has seen strong growth in research activity in recent years. Drivers for such growth include rapidly increasing customer expectations for quality, mobility, and functionality for audio in communications and entertainment scenarios. Many consumers have come to expect that there is really nothing that cannot be done with audio, whether it’s music or speech, providing your battery holds out. This “demand” is coupled with the “supply” of low-cost, high-performance audio processing capability. The number of AASP submissions to ICASSP grew again this year by 25% with papers in speech enhancement, source separation, array processing, and music analysis being the most numerous. Similar trends can be seen for IEEE Transactions on Audio, Speech, and Language Processing.
Time-Based Compression and Classification of Heartbeats

Heart function measured by electrocardiograms (ECG) is crucial for patient care. ECG generated waveforms are used to find patterns of irregularities in cardiac cycles in patients. In many cases, irregularities evolve over an extended period of time that requires continuous monitoring. However, this requires wireless ECG recording devices. These devices consist of an enclosed system that includes electrodes, processing circuitry, and a wireless communication block imposing constraints on area, power, bandwidth, and resolution. In order to provide continuous monitoring of cardiac functions for real-time diagnostics, we propose a methodology that combines compression and analysis of heartbeats. The signal encoding scheme is the time-based integrate and fire sampler. The diagnostics can be performed directly on the samples avoiding reconstruction required by the competing finite rate of innovation and compressed sensing. As an added benefit, our scheme provides an efficient hardware implementation and a compressed representation for the ECG recordings, while still preserving discriminative features. We demonstrate the performance of our approach through a heartbeat classification application consisting of normal and irregular heartbeats known as arrhythmia. Our approach that uses simple features extracted from ECG signals is comparable to results in the published literature.
Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences

Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. We apply these kernels to construct measures of divergence between two point processes and use them for hypothesis testing, that is, to observe if two sets of spike trains originate from the same underlying probability law. Although there exist positive-definite spike train kernels in the literature, we establish that these kernels are not strictly definite and thus do not induce measures of divergence. We discuss the properties of both of these existing nonstrict kernels and the novel strict kernels in terms of their computational complexity, choice of free parameters, and performance on both synthetic and real data through kernel principal component analysis and hypothesis testing.
Entropy minimization for supervised digital communications channel equalization

This paper investigates the application of error-entropy minimization algorithms to digital communications channel equalization. The pdf of the error between the training sequence and the output of the equalizer is estimated using the Parzen windowing method with a Gaussian kernel, and then, the Renyi's quadratic entropy is minimized using a gradient descent algorithm. By estimating Renyi's entropy over a short sliding window, an online training algorithm is also introduced. Moreover, for a linear equalizer, an orthogonality condition for the minimum entropy solution that leads to an alternative fixed-point iterative minimization method is derived. The performance of linear and nonlinear equalizers trained with entropy and mean square error (MSE) is compared. As expected, the results of training a linear equalizer are very similar for both criteria since, even if the input noise is non-Gaussian, the output filtered noise tends to be Gaussian. On the other hand, for nonlinear channels and using a multilayer perceptron (MLP) as the equalizer, differences between both criteria appear. Specifically, it is shown that the additional information used by the entropy criterion yields a faster convergence in comparison with the MSE.
Sequential causal estimation and learning from time-varying images

Dynamic models are used in modeling the perceptual systems with hierarchies. But most of the models assume Gaussian statistics on the underlying causes. In this paper we try to develop a basic building block for these hierarchical models where the causes are assumed to be non-Gaussian. We describe a sequential dual estimation framework for inferring the hidden states and unknown causes/inputs while learning the parameters of the model. It is observed that the algorithm is able to extract bases from the time varying image sequence that resembles receptive fields of the simple cells in V1. In addition, the dynamical model gives us the ability to deconvolve spatial and temporal changes in the image sequence.
Evaluation of a BSS algorithm for artifacts rejection in epileptic seizure detection

A data efficient blind sources separation (BSS) algorithm has been applied to preprocess intracranial EEG (ECoG) for artifact rejection. After artifacts correction a recurrence time statistics T1 feature was evaluated from the 'cleaned' data. Seizure detection performance was compared between BSS preprocessing and without preprocessing. Test results show that in a data set, for a detection rate of 96%, the false alarm rate dropped from 0.13 per hour without BSS preprocessing to 0.08 with preprocessing. For the other set of data, the false alarm rate dropped from 0.34 to 0.21 at a detection rate of 100%.
Hidden state estimation using the Correntropy Filter with fixed point update and adaptive kernel size

In this paper we review the Correntropy Filter for hidden state estimation and we introduce the fixed point update rule for the Correntropy Filter instead of using gradient ascent for faster convergence. We further propose an adaptive kernel bandwidth selection algorithm. It is shown that the new filter outperforms the Kalman Filter and has no free parameters. The algorithm's capabilities are demonstrated on a simulated experiment and a vehicle tracking problem.
Convolutive blind source separation by minimizing mutual information between segments of signals

A method to perform convolutive blind source separation of super-Gaussian sources by minimizing the mutual information between segments of output signals is presented. The proposed approach is essentially an implementation of an idea previously proposed by Pham. The formulation of mutual information in the proposed criterion makes use of a nonparametric estimator of Renyi's /spl alpha/-entropy, which becomes Shannon's entropy in the limit as /spl alpha/ approaches 1. Since /spl alpha/ can be any number greater than 0, this produces a family of criteria having an infinite number of members. Interestingly, it appears that Shannon's entropy cannot be used for convolutive source separation with this type of estimator. In fact, only one value of /spl alpha/ appears to be appropriate, namely /spl alpha/=2, which corresponds to Renyi's quadratic entropy. Four experiments are included to show the efficacy of the proposed criterion.
Quantized Attention-Gated Kernel Reinforcement Learning for Brain–Machine Interface Decoding

Reinforcement learning (RL)-based decoders in brain–machine interfaces (BMIs) interpret dynamic neural activity without patients’ real limb movements. In conventional RL, the goal state is selected by the user or defined by the physics of the problem, and the decoder finds an optimal policy essentially by assigning credit over time, which is normally very time-consuming. However, BMI tasks require finding a good policy in very few trials, which impose a limit on the complexity of the tasks that can be learned before the animal quits. Therefore, this paper explores the possibility of letting the agent infer potential goals through actions over space with multiple objects, using the instantaneous reward to assign credit spatially. A previous method, attention-gated RL employs a multilayer perceptron trained with backpropagation, but it is prone to local minima entrapment. We propose a quantized attention-gated kernel RL (QAGKRL) to avoid the local minima adaptation in spatial credit assignment and sparsify the network topology. The experimental results show that the QAGKRL achieves higher successful rates and more stable performance, indicating its powerful decoding ability for more sophisticated BMI tasks as required in clinical applications.
Modeling time dependencies in the mixture of experts

The mixture of experts, as it was originally formulated, is a static algorithm in the sense that the output of the network, and parameter updates during training, are completely independent from one time step to the next. This independence creates difficulties when the model is applied to time series prediction. We address this by adding memory to the mixture of experts. A Gaussian assumption on each expert's error is replaced by a chi-square distribution on the local (in time) root mean square error. We derive new gradient descent equations, and present a simulation that demonstrates an improvement in the segmentation of a time series over the classical algorithm.
Survival kernel with application to kernel adaptive filtering

In this paper, we define a new Mercer kernel, namely survival kernel, which is closely related to our recently proposed survival information potential (SIP). The new kernel function is parameter free, simple in calculation, and strictly positive-definite (SPD) over ℝm+, hence it has potential utility in machine learning especially in online kernel learning. In this work we apply the survival kernel to kernel adaptive filtering, in particular the kernel least mean square (KLMS) algorithm. Simulation results show that KLMS with survival kernel may achieve satisfactory performance with little computational time and without the choice of free parameters.
Kernel Methods on Spike Train Space for Neuroscience: A Tutorial

Over the last decade, several positive-definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain a mere curiosity for both computational neuroscientists and signal processing experts. This tutorial illustrates why kernel methods can, and have already started to, change the way spike trains are analyzed and processed. The presentation incorporates simple mathematical analogies and convincing practical examples in an attempt to show the yet unexplored potential of positive definite functions to quantify point processes. It also provides a detailed overview of the current state of the art and future challenges with the hope of engaging the readers in active participation.
Chaotic activity during iron-induced 'epileptiform' discharge in rat hippocampal slices

Low-dimensional chaotic dynamics have been suggested in the rat hippocampal slice during iron-induced epileptiform activity. The dimensionality of this chaotic activity has been found to be similar in slices bathed in the same ionic extracellular medium. Some slices also displayed a drop in dimensionality prior to the onset of seizure-like activity. It is suggested that techniques of nonlinear dynamical analysis are a useful reverse-engineering tool for studying the in-vitro brain slice. The authors further conclude that neuronal circuits capable of displaying activity could exist at the level of the in vitro brain slice.<<ETX>>
A CFAR intensity pattern detector for MMW SAR images

In this paper, we first demonstrate a novel CFAR intensity pattern matching detector, which is formulated based on the generalized likelihood ratio test (GLRT) for target detection in millimeter wave (MMW) SAR images. Both the widely used two-parameter CFAR and the /spl gamma/-CFAR are the special cases of the intensity matching detector with different intensity kernels to be matched. Secondly, to find the best intensity matching kernel, we propose to apply principal component analysis (PCA) to the radial intensity profiles of several targets. It can be experimentally shown for the MSTAR data set that the first eigenfunction of the radial intensity profile of targets can be well approximated by the first order Gamma kernel, which provides the optimal matching intensity kernel. Therefore, the /spl gamma/-CFAR detector approximately performs "normalized" maximum eigenfiltering, resulting in better performance when compared with the delta function stencil proposed by MIT/Lincoln Laboratories.