nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of stress reactivity and recovery using biosignals and fuzzy theory

This paper is about the evaluation of stress reactivity and recovery using biosignals and fuzzy theory. We induced mental stress by means of a coin-stacking task. During the experiment, 4 kinds of biosignals, which are frontalis EMG, ECG, peripheral skin temperature and skin conductance level, were acquired. Then, the degree of stress was assessed by synthetically analyzing those signals using fuzzy inference. From the fuzzy inference result, the parameters (Amount of Physiological Change/Amount of Imposed Stress) and (Time to 25% Recovery), which represent reactivity and recovery respectively, were derived. We made a graph using the reactivity parameter as an abscissa and the recovery parameter as an ordinate for each subject. From the graph, the distance from the origin to the coordinate ((Amount of Physiological Change/Amount of Imposed Stress), (Time to 25% Recovery)) was introduced as a stress index. An insensitive reactivity and a fast recovery is an effective coping with stress. Therefore, the small value of the stress index proposed in this research will indicate being in good health.
Structural risk minimization for robust blind identification of sparse SIMO channels

In this paper the structural risk minimization (SRM) principle is applied to derive an iterative algorithm for blind identification of sparse single-input multiple-output (SIMO) channels. The key idea consists of reformulating this problem as a support vector regression (SVR) problem in which the channel coefficients are the Lagrange multipliers of the dual problem. By employing the Vapnik's e-insensitivity loss function, the solution can be expanded in terms of a reduced number of Lagrange multipliers (i.e., the nonzero filter coefficients) and then a sparse solution is found. This method can be also used for non-sparse channels when the channel order has been highly overestimated. In this situation, the SRM principle pushes zero to the small leading and trailing terms of the impulse response. Some simulation results are provided to demonstrate the performance of the method.
Wireless Transmission of Neural Signals Using Entropy and Mutual Information Compression

Two of the most critical tasks when designing a portable wireless neural recording system are to limit power consumption and to efficiently use the limited bandwidth. It is known that for most wireless devices the majority of power is consumed by the wireless transmitter and it often represents the bottleneck of the overall design. This paper compares two compression techniques that take advantage of the sparseness of the neural spikes in neural recordings using an information theoretic formalism to enhance the well-established vector quantization (VQ) algorithm. The two discriminative VQ algorithms are applied to neuronal recordings proving their ability to accurately reconstruct action potential (AP) regions of the neuronal signal while compressing background activity without using thresholds. The two operational modes presented offer distinct characteristics to lossy compression. The first approach requires no preprocessing or prior knowledge of the signal while the second requires a training set of spikes to obtain AP templates. The compression algorithms are implemented on an on-board digital signal processor (DSP) and results show that power consumption is decreased while the bandwidth is more efficiently utilized. The compression algorithms have been tested in real time on a hardware platform (PICO DSP ) enhanced with the DSP which runs the algorithm before sending the compressed data to a wireless transmitter. The compression ratios obtained range from 70:1 and 40:1 depending on the signal to noise ratio (SNR) of the input signal. The spike sorting accuracy in the reconstructed data is 95% compatible to the original neural data.
Cognitive Architectures for Sensory Processing

This paper describes our efforts to design a cognitive architecture for object recognition in video. Unlike most efforts in computer vision, our work proposes a Bayesian approach to object recognition in video, using a hierarchical, distributed architecture of dynamic processing elements that learns in a self-organizing way to cluster objects in the video input. A biologically inspired innovation is to implement a top-down pathway across layers in the form of causes, creating effectively a bidirectional processing architecture with feedback. To simplify discrimination, overcomplete representations are utilized. Both inference and parameter learning are performed using empirical priors, while imposing appropriate sparseness constraints. Preliminary results show that the cognitive architecture has features that resemble the functional organization of the early visual cortex. One example showing the use of top-down connections is given to disambiguate a synthetic video from correlated noise.
VLSI silicon based prosthesis for in vitro measurement of neural activity

VLSI integrated circuits have been fabricated and tested for the measurement of neural activity of in vitro brain slices. The circuits were fabricated using MOSIS fabrication services. To date two CMOS IC structures have been fabricated. The first is a passive sensor array used to assess the compatibility of the electrodes with biological tissue and sensor reliability. The second IC contains switched-capacitor amplifiers with polysilicon input electrodes.<<ETX>>
The Kernel Least-Mean-Square Algorithm

The combination of the famed kernel trick and the least-mean-square (LMS) algorithm provides an interesting sample-by-sample update for an adaptive filter in reproducing kernel Hilbert spaces (RKHS), which is named in this paper the KLMS. Unlike the accepted view in kernel methods, this paper shows that in the finite training data case, the KLMS algorithm is well posed in RKHS without the addition of an extra regularization term to penalize solution norms as was suggested by Kivinen [Kivinen, Smola and Williamson, ldquoOnline Learning With Kernels,rdquo  2004] and Smale [Smale and Yao, ldquoOnline Learning Algorithms,rdquo Foundations in Computational Mathematics, vol. 6, no. 2, pp. 145-176, 2006]. This result is the main contribution of the paper and enhances the present understanding of the LMS algorithm with a machine learning perspective. The effect of the KLMS step size is also studied from the viewpoint of regularization. Two experiments are presented to support our conclusion that with finite data the KLMS algorithm can be readily used in high dimensional spaces and particularly in RKHS to derive nonlinear, stable algorithms with comparable performance to batch, regularized solutions.
Assessing Granger Non-Causality Using Nonparametric Measure of Conditional Independence

In recent years, Granger causality has become a popular method in a variety of research areas including engineering, neuroscience, and economics. However, despite its simplicity and wide applicability, the linear Granger causality is an insufficient tool for analyzing exotic stochastic processes such as processes involving non-linear dynamics or processes involving causality in higher order statistics. In order to analyze such processes more reliably, a different approach toward Granger causality has become increasingly popular. This new approach employs conditional independence as a tool to discover Granger non-causality without any assumption on the underlying stochastic process. This paper discusses the concept of discovering Granger non-causality using measures of conditional independence, and proposes a novel measure of conditional independence. In brief, the proposed approach estimates the conditional distribution function through a kernel based least square regression approach. This paper also explores the strengths and weaknesses of the proposed method compared to other available methods, and provides a detailed comparison of these methods using a variety of synthetic data sets.
A hybrid subspace projection method for system identification

Principal components analysis (PCA), being the most optimal linear mapper in a least-squares (LS) sense, has been predominantly used in subspace-based signal processing methods. In system identification problems, optimal subspace projections must span the joint space of the input and output of the unknown system. In this scenario, subspaces determined by the principal components of the input or the desired signal alone do not embed key information, which lies in the joint space. We first propose a hybrid subspace projection method that finds optimal projections in the joint space. The concepts behind this method are firmly rooted in statistical theory. We then derive adaptive learning algorithms to estimate the subspace projections. Finally, we show the superiority of the new framework in solving system identification problems in noisy environments.
Hierarchical Linear Dynamical Systems: A new model for clustering of time series

The auditory cortex in the brain does effortlessly a better job of extracting information from the acoustic world than our current generation of signal processing algorithms. The proposed architecture, Hierarchical Linear Dynamical System (HLDS), is based on Kalman filters with hierarchically coupled state models that stabilize the input dynamics and provide a representation space. This approach extracts information from the input and self-organizes it in the higher layers leading to an algorithm capable of clustering time series in an unsupervised manner. In this paper we further investigate the properties of HLDS, demonstrate its performance on music rather than isolated notes and propose the time domain implementation to overcome one of its current bottlenecks.
Modeling of Synchronized Burst in Dissociated Cortical Tissue: An Exploration of Parameter Space

In vitro neuronal culture is a potentially powerful tool for investigation of information processing of neurons. However, in a mature dissociated neuronal culture, synchronized bursting is often observed. Bursting strongly drives the dynamics of the network, thus making it difficult to investigate the computational properties. A quantitative modeling of the dynamics would provide a means for understanding the processes that produce bursts and a means to manipulate the dynamics to control them. A simulated network composed of leaky integrate and Are neurons and dynamic synapses is used to model the phenomenon. We investigate the variability of burst dynamics according to parameters such as connectivity, recovery time constant, and noise.
A Wireless Power Interface for Rechargeable Battery Operated Neural Recording Implants

This paper describes an integrated analog front-end for wireless powering and recharging of miniature Li-ion batteries used in implantable neural recording microsystems. DC signal extraction from a wireless carrier is accomplished using Schottky barrier contact diodes with lower forward voltage drop for improved efficiency. The battery charger employs a new control loop that relaxes comparator resolution requirements, provides simultaneous operation of constant-current and constant-voltage loops, and eliminates the external current sense resistor from the charging path. The accuracy of the end-of-charge detection is primarily determined by the voltage drop across matched resistors and current-sources and the offset voltage of the sense comparator. Experimental results in 0.6 mum bulk CMOS technology indicate that plusmn1.3% (or plusmn20 muA) end-of-charge accuracy can be obtained under worst-case conditions for a comparator offset voltage of plusmn5 mV. The circuits occupy 1.735 mm2 with a power dissipation of 8.4 mW when delivering a load current of 1.5 mA at 4.1 V (or 6.15 mW) for an efficiency of 73%
Robust EEG preprocessing for dependence-based condition discrimination

This paper addresses the robustness of the filtering schemes in processing high resolution electroencephalogram (EEG) data in the context of discriminating two stimuli flickering at a given frequency. The raw data consists of recordings from a 128-channel HydroCell GSN where the subject was visually stimulated with two images flickering at 17.5 Hz, representing two distinct conditions, referred to as Face and Mock. These signals were then passed through a band pass filter to only capture the modulation at the flickering frequency, and a connectivity analysis was performed on the filtered signal using generalized measure of association, to observe if the network connectivity changes from one stimulus to the other. In this paper, we investigate the effect of the bandpass filter on the discriminability of the stimuli over different filter orders and quality factors. We observe that the network connectivity is stable over a significant range of parameter values of the filter, thus establishing the desired robustness.
Wireless, In Vivo Neural Recording using a Custom Integrated Bioamplifier and the Pico System

This paper describes a wireless system for sampling multiple channels of neural activity based on a low-power, custom 80dB-gain integrated bioamplifier, Texas Instrument's MSP430 microprocessors, and Nordic Semiconductor's ultra low power, high bandwidth RF transmitter/receivers. The system's features are presented as well as results of spike potentials from a live subject.
Modified Kalman filter based method for training state-recurrent multilayer perceptrons

Kalman filter based training algorithms for recurrent neural networks provide a clever alternative to the standard backpropagation in time. However, these algorithms do not take into account the optimization of the hidden state variables of the recurrent network. In addition, their formulation requires Jacobian evaluations over the entire network, adding to their computational complexity. We propose a spatial-temporal extended Kalman filter algorithm for training recurrent neural network weights and internal states. This new formulation also reduces the computational complexity of Jacobian evaluations drastically by decoupling the gradients of each layer. Monte Carlo comparisons with backpropagation through time point out the robust and fast convergence of the algorithm.
Density-dependent quantized kernel least mean square

Kernel least mean square is a simple and effective adaptive algorithm, but dragged by its unlimited growing network size. Many schemes have been proposed to reduce the network size, but few takes the distribution of the input data into account. Input data distribution is generally important in view of both model sparsification and generalization performance promotion. In this paper, we introduce an online density-dependent vector quantization scheme, which adopts a shrinkage threshold to adapt its output to the input data distribution. This scheme is then incorporated into the quantized kernel least mean square (QKLMS) to develop a density-dependent QKLMS (DQKLMS). Experiments on static function estimation and short-term chaotic time series prediction are presented to demonstrate the desirable performance of DQKLMS.
Nearest Neighbor Distributions for imbalanced classification

The class imbalance problem is pervasive in machine learning. To accurately classify the minority class, current methods rely on sampling schemes to close the gap between classes, or on the application of error costs to create algorithms which favor the minority class. Since the sampling schemes and costs must be specified, these methods are highly dependent on the class distributions present in the training set. This makes them difficult to apply in settings where the level of imbalance changes, such as in online streaming data. Often they cannot handle multi-class problems. We present a novel single-class algorithm called Class Conditional Nearest Neighbor Distribution (CCNND), which mitigates the effects of class imbalance through local geometric structure in the data. Our algorithm can be applied seamlessly to problems with any level of imbalance or number of classes, and new examples are simply added to the training set. We show that it performs as well as or better than top sampling and cost-weighting methods on four imbalanced datasets from the UCI Machine Learning Repository, and then apply it to streaming data from the oil and gas industry alongside a modified nearest neighbor algorithm. Our algorithm's competitive performance relative to the state-of-the-art, coupled with its extremely simple implementation and automatic adjustment for minority classes, demonstrates that it is worth further study.
Florida Wireless Implantable Recording Electrodes (FWIRE) for Brain Machine Interfaces

This paper reviews on-going efforts towards the development of the Florida wireless implantable recording electrodes (FWIRE). The FWIRE microsystem platform is a fully implantable flexible substrate microelectrode array that employs state-of-the-art integrate-and-fire (IF) signal representation and wireless interface circuitry for recording neural activity from behaving rodents. The modular nature of the implantable neural recording electrode allows future enhancements to be seamlessly added to improve functionality including but not limited to rechargeability through inductive coupling, custom microelectrode arrays, higher capacity batteries, and more advanced integrated circuit technologies. This paper concentrates on custom integrated circuits such as neural interfacing amplifiers, baseband signal processing, wireless data and power interfaces, and battery management system.
Efficient temporal decomposition of local field potentials

Local field potentials (LFPs) arise from dendritic currents that are summed by the brain tissue's impedance. By assuming that the rhythms existing in the LFPs result from the coordinated neural activity of sparse and transient neural assemblies transformed by the neural tissue, we propose to recover these neural assemblies sources using an independent component analysis on segments of a single LFP channel. The corresponding source signals and the set of temporal filters that operate on them constitute an efficient time-frequency decomposition of the LFP. This decomposition has the potential to identify sources that are more statistically dependent with stimuli or single-cell activity than the raw signal. In this work we show preliminary results on a synthetic dataset and a real dataset recorded from a rats nucleus accumbens during a reward administering experiment. When compared with the standard time-frequency analysis, this computational model for LFP analysis is totally data-driven because the filters, which form the basis for the decomposition, are estimated directly from the data.
Independent components analysis using Renyi's mutual information and Legendre density estimation

We have previously proposed the use of quadratic Renyi's mutual information (1970), estimated using Parzen windowing, as an ICA criterion and showed that it utilizes data more efficiently than classical algorithms like InfoMax and FastICA. We suggested the use of Renyi's definition of information theoretic quantities rather than Shannon's definitions since Shannon's definitions are already included in Renyi's as special cases. In the estimation of probability densities using kernel methods, the choice of the kernel width is an important issue that affects the overall performance of the system, and there is no known way of determining the optimal value. Legendre polynomial expansion of a probability distribution, on the other hand, has two advantages. Hardware implementation is trivial and it does not require the choice of any parameter except for the point of truncation of the series. The rule for this assignment is simple: the longer the series, the more accurate the density estimation becomes. Thus, we combine these two schemes, namely Renyi's entropy and Legendre polynomial expansion for probability density function estimation to obtain a simple ICA algorithm. This algorithm is then tested on blind source separation, time-series analysis, and data reduction.
An Associative Memory Readout in ESN for Neural Action Potential Detection

This paper describes how echo state networks (ESN) can be used in conjunction with minimum average correlation energy (MACE) filters in order to create a system that can identify spikes in neural recordings. Various experiments using real-world data were used to compare the performance of the ESN-MACE against threshold and matched filter detectors to ascertain the capabilities of such a system in detecting neural action potentials. The experiments demonstrate that the ESN-MACE can correctly detect spikes with lower false alarm rates than established detection techniques since it captures the inherent variability and the covariance information in spike shapes by training.
Separating spatial and temporal activation patterns in fMRI using competitive subspace projection

The challenge for functional magnetic resonance imaging (fMRI) is to determine when and where the response due to an external stimulus occurs in the images. The temporal clustering analysis (TCA) method has been used to study brain activity after eating and drinking, in both the time and spatial domains. We propose a new method, competitive subspace projection (CSP), to represent data optimally compared to other subspace projection methods. This method is used to detect spatial and temporal activation patterns in fMRI associated with such behavior. The CSP and TCA methods are compared using both synthetic and real fMRI data. The results on both data sets show consistent conclusions can be drawn from these two methods while CSP is observed to have a better noise rejection capability than TCA.
Training neural networks with additive noise in the desired signal

A new global optimization strategy for training adaptive systems such as neural networks and adaptive filters (finite or infinite impulse response (FIR or IIR)) is proposed in this paper. Instead of adding random noise to the weights as proposed in the past, additive random noise is injected directly into the desired signal. Experimental results show that this procedure also speeds up greatly the backpropagation algorithm. The method is very easy to implement in practice, preserving the backpropagation algorithm and requiring a single random generator with a monotonically decreasing step size per output channel. Hence, this is an ideal strategy to speed up supervised learning, and avoid local minima entrapment when the noise variance is appropriately scheduled.
Display of EEG chaotic dynamics

A method for the visualization of the electroencephalogram (EEG) in phase space is described. The signal is transformed from the time domain to phase space by measuring the correlation dimension of the data and constructing the attractor in the required embedding space, which is normally of dimension 3 to 8. A singular value decomposition is used to obtain a good projection to 3-D. Animation of a series of 2-D rotated projections provides a way to visualize the fine structure of the reconstructed 3-D attractor. Examples and a computer package are briefly described.<<ETX>>