nils-herrmann/clean_scirepeval huggingface dataset:
Quantifying cognitive state from EEG using phase synchrony

Phase synchrony is a powerful amplitudeindependent measure that quantifies linear and nonlinear dynamics between non-stationary signals. It has been widely used in a variety of disciplines including neural science and cognitive psychology. Current time-varying phase estimation uses either the Hilbert transform or the complex wavelet transform of the signals. This paper exploits the concept of phase synchrony as a mean to discriminate face processing from the processing of a simple control stimulus. Dependencies between channel locations were assessed for two separate conditions elicited by distinct pictures (representing a human face and a Gabor patch), both flickering at a rate of 17.5 Hz. Statistical analysis is performed using the Kolmogorov-Smirnov test. Moreover, the phase synchrony measure used is compared with a measure of association that has been previously applied in the same context: the generalized measure of association (GMA). Results show that although phase synchrony works well in revealing regions of high synchronization, and therefore achieves an acceptable level of discriminability, this comes at the expense of sacrificing time resolution.
Plenary speakers: Reliable interfaces with the peripheral nervous system

The following topics are discussed: Neural Robotics and Rehabilitation; Suspicious Coincidences in the Brain; Deep Brain Stimulation - Challenges and Opportunities; Translational Neuroengineering;  A Cognitive Neural Prosthesis for Restoring and Enhancing Memory Function; Brain Dynamics of Human Motor Control: Neuromorphic Modeling and Brain-Machine-Body Interfaces; Neuroprosthetic Technologies to Improve Functional Recovery after Neuromotor Disorders; Translational Research Tools for Investigating Neurological Disease; Neural Control and Modulation; Neuromodulation of the Spinal Circuitry to Regain Motor Function after Spinal Cord Injury; Neural Excitation-Driven Musculo-Skeletal Modeling for Rehabilitation Technologies; Brain-Computer Interface based on Visual Modulation;  Restoring Useful and Functional Movements with FES is Still a Big Challenge: From Past to Present Some Big Issues; Bioengineered Artificial Retina; MRI Measured Brain Connectivities and Their Applications; Update on TMR with Pattern Recognition for Control of Powered Prostheses; High Resolution Recording and Data Mining in Epileptic Networks; Challenges in Increasing the Resolution and Device Longevity of a Retinal Prosthesis; Novel Approaches in Neural and Rehabilitation Engineering; Development of Biomimetic Cortical Interfaces for the Restoration of Sensorimotor Function; Neuroprostheses for Movement Restoration; Tensor Product Kernels for Multiscale Neural Decoding and Control; Neurally and Ocularly Informed Graph-Based Models for Searching 3D Environments; Towards Model-Based Control in Neural Engineering; Translating Revolutionary Prosthesis to Practical Prosthesis; Are Our Cellular Models Fundamentally Wrong?; and Electrophysiological Biomarkers of Human Epileptogenic Brain.
Blind source separation using Renyi's mutual information

A blind source separation algorithm is proposed that is based on minimizing Renyi's mutual information by means of nonparametric probability density function (PDF) estimation. The two-stage process consists of spatial whitening and a series of Givens rotations and produces a cost function consisting only of marginal entropies. This formulation avoids the problems of PDF inaccuracy due to truncation of series expansion and the estimation of joint PDFs in high-dimensional spaces given the typical paucity of data. Simulations illustrate the superior efficiency, in terms of data length, of the proposed method compared to fast independent component analysis (FastICA), Comon's (1994) minimum mutual information, and Bell and Sejnowski's (1995) Infomax.
Spike Sorting Using non Parametric Clustering VIA Cauchy Schwartz PDF Divergence

We propose a new method of clustering neural spike waveforms for spike sorting. After detecting the spikes using a threshold detector, we use principal component analysis (PCA) to get the first few PCA components of the data. Clustering on these PCA components is achieved by maximizing the Cauchy Schwartz PDF divergence measure which uses the Parzen window method to non parametrically estimate the PDF of the clusters. Comparison with other clustering techniques in spike sorting like k-means and Gaussian mixture elucidates the superiority of our method in terms of classification results and computational complexity
Face classification using a multiresolution principal component analysis

Multiresolution principal component analysis (M-PCA) uses principal component analysis (PCA) to obtain multiresolution features for a signal. Bischof (1995) and Bischof and Hornik (1996) used 3-layer networks to train principal component pyramids for image compression. M-PCA uses a single computational layer adaptive linear network trained with the generalized Hebbian algorithm (GHA). The multiresolution features were applied to automatic face recognition and tested against the Olivetti Research Lab database. Classification with multiresolution had an average (over 10 runs) error rate of 2.4%.
Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns

Linear and nonlinear (TDNN) models have been shown to estimate hand position using populations of action potentials collected in the pre-motor and motor cortical areas of a primate's brain. One of the applications of this discovery is to restore movement in patients suffering from paralysis. For real-time implementation of this technology, reliable and accurate signal processing models that produce small error variance in the estimated positions are required. In this paper, we compare the mapping performance of the FIR filter, gamma filter and recurrent neural network (RNN) in the peaks of reaching movements. Each approach has strengths and weaknesses that are compared experimentally. The RNN approach shows very accurate peak position estimations with small error variance.
The integrate-and-fire sampler: A special type of asynchronous Σ - Δ modulator

We present the integrate-and-fire (IF) model as a time encoding scheme and show that it corresponds to a special case of the asynchronous Σ-Δ modulator (ASDM). We depart from typical rate based encoding used by conventional ASDM towards time based encoding. We show that for certain families of signals, such as neural recordings, the IF sampler accurately encodes the regions of interest at sub-Nyquist data rates. Nevertheless, the encoder requires nonlinear recovery algorithms, instead of the typical low pass filter.
Online Active Learning for Automatic Target Recognition

Automatic target recognition in sidescan sonar imagery is vital to many applications, particularly sea mine detection and classification. We expand upon the traditional offline supervised classification approach with an active learning method to automatically label new objects that are not present in the training set. This is facilitated by the option of sending difficult samples to an outlier bin, from which models can be built for new objects. The decisions of the classifier are improved by a novel active learning approach, called model trees (MT), which builds an ensemble of hypotheses about the classification decisions that grows proportionally to the amount of uncertainty the system has about the samples. Our system outperforms standard active learning methods, and is shown to correctly identify new objects much more accurately than a pure clustering approach, on a simulated sidescan sonar data set.
Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion

The steady-state excess mean square error (EMSE) of the adaptive filtering under the maximum correntropy criterion (MCC) has been studied. For Gaussian noise case, we establish a fixed-point equation to solve the exact value of the steady-state EMSE, while for non-Gaussian noise case, we derive an approximate analytical expression for the steady-state EMSE, based on a Taylor expansion approach. Simulation results agree with the theoretical calculations quite well.
A Generalized Methodology for Data Analysis

Based on a critical analysis of data analytics and its foundations, we propose a functional approach to estimate data ensemble properties, which is based entirely on the empirical observations of discrete data samples and the relative proximity of these points in the data space and hence named empirical data analysis (EDA). The ensemble functions include the nonparametric square centrality (a measure of closeness used in graph theory) and typicality (an empirically derived quantity which resembles probability). A distinctive feature of the proposed new functional approach to data analysis is that it does not assume randomness or determinism of the empirically observed data, nor independence. The typicality is derived from the discrete data directly in contrast to the traditional approach, where a continuous probability density function is assumed a priori. The typicality is expressed in a closed analytical form that can be calculated recursively and, thus, is computationally very efficient. The proposed nonparametric estimators of the ensemble properties of the data can also be interpreted as a discrete form of the information potential (known from the information theoretic learning theory as well as the Parzen windows). Therefore, EDA is very suitable for the current move to a data-rich environment, where the understanding of the underlying phenomena behind the available vast amounts of data is often not clear. We also present an extension of EDA for inference. The areas of applications of the new methodology of the EDA are wide because it concerns the very foundation of data analysis. Preliminary tests show its good performance in comparison to traditional techniques.
Echo State Networks for Motor Control of Human ECoG Neuroprosthetics

Towards non-invasive neuroprosthetic systems for motor control, electrocorticogram (ECoG) recordings provide an intermediate step from microwire single neuron recordings to electroencephalograms. Preprocessing modalities that emphasize amplitude modulation and temporal power in the ECoG are employed to build human brain-machine interfaces, which have been previously shown to modulate with hand behavior by means of linear filters, though, with limited accuracy. We improve the online decoding performance of the amplitude modulation across the recording spectra by employing echo state networks (ESNs) which provide nonlinear mappings without compromising training complexity and filter order compared to basic linear filters and other neural networks. Preliminary results show an increase of 15% in the average correlation of ESN outputs with actual hand trajectories compared to linear mappings.
An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters

This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.
A Reconfigurable Neural Signal Processor (NSP) for Brain Machine Interfaces

In this paper, we present a design for a wearable computational DSP system that alleviates the issues of a previous design and provides a much smaller and lower power solution for the overall BMI goals. The system first acquires the neural data through a high speed data bus in order to train and evaluate prediction models. Then it wirelessly transmits the predicted results to a simulated robot arm. This system has been built and successfully tested with real and simulated data
Microcomputer-based digital filters for EEG processing

Fourth-order bandpass recursive digital filters, realized in cascade form, are implemented in a 16-bit microprocessor, enabling sampling frequencies up to 3.3 kHz, with the use of the processor's multiply instruction. The design did not follow the usual choice of canonical forms and scaling within the filter structure. On the contrary, the input wordlength is set at 12 bits and the filters are allowed to have gains up to 16. This design method provided the required output signal-to-noise ratio and shorter computation time when compared to the scaling design, since the filters can be realized with only 4 multiplications per cycle. It was found that the filter's internal magnification is a function of the transformation used to arrive at the filter Z plane transfer function. The design parameters for the filters used in the analysis of sleep electroencephalograms (EEG's) are also presented.
Empirical data analysis: A new tool for data analytics

In this paper, a novel empirical data analysis approach (abbreviated as EDA) is introduced which is entirely data-driven and free from restricting assumptions and pre-defined problem- or user-specific parameters and thresholds. It is well known that the traditional probability theory is restricted by strong prior assumptions which are often impractical and do not hold in real problems. Machine learning methods, on the other hand, are closer to the real problems but they usually rely on problem- or user-specific parameters or thresholds making it rather art than science. In this paper we introduce a theoretically sound yet practically unrestricted and widely applicable approach that is based on the density in the data space. Since the data may have exactly the same value multiple times we distinguish between the data points and unique locations in the data space. The number of data points, k is larger or equal to the number of unique locations, l and at least one data point occupies each unique location. The number of different data points that have exactly the same location in the data space (equal value), ƒ can be seen as frequency. Through the combination of the spatial density and the frequency of occurrence of discrete data points, a new concept called multimodal typicality, τMM is proposed in this paper. It offers a closed analytical form that represents ensemble properties derived entirely from the empirical observations of data. Moreover, it is very close (yet different) from the histograms, from the probability density function (pdf) as well as from fuzzy set membership functions. Remarkably, there is no need to perform complicated pre-processing like clustering to get the multimodal representation. Moreover, the closed form for the case of Euclidean, Mahalanobis type of distance as well as some other forms (e.g. cosine-based dissimilarity) can be expressed recursively making it applicable to data streams and online algorithms. Inference/estimation of the typicality of data points that were not present in the data so far can be made. This new concept allows to rethink the very foundations of statistical and machine learning as well as to develop a series of anomaly detection, clustering, classification, prediction, control and other algorithms.
Synchronization analysis of epileptic ECOG data using SOM-based SI measure

The exact spatio-temporal changes leading to epileptic seizures, although widely studied, are not well understood yet. We propose to investigate the mechanisms leading to epileptic seizures by using a self-organising map (SOM) based similarity index (SI) measure. While it is shown that this measure is statistically as accurate as the original SI measure, it is also computationally faster and therefore applicable for real-time analyses. Application of SOM-based SI measure on epileptic seizure data reveals interesting aspects of synchronization and de-synchronization at various spatio-temporal levels.
Clustering using Renyi's entropy

We propose a new clustering algorithm using Renyi's entropy as our similarity metric. The main idea is to assign a data pattern to the cluster, which among all possible clusters, increases its within-cluster entropy the least, upon inclusion of the pattern. We refer to this procedure as differential entropy clustering. Not knowing the true number of clusters in advance, initially a number of small clusters are "seeded" randomly in the data set, labeling a small subset of the data. Thereafter all remaining patterns are labeled by differential entropy clustering. Subsequently, we identify the "worst cluster" by a quantity we name as the between-cluster entropy. Its members are re-clustered, again by differential entropy clustering, reducing the overall number of clusters by one. This procedure is repeated until only two clusters remain. At each step we store the current labels, thus producing a hierarchy of clusters. The between-cluster entropy also enables us to select our final set of clusters in other cluster hierarchy. We demonstrate the clustering algorithm when applied both to artificially created data sets and a real data set.
A model based approach to exploration of continuous-state MDPs using Divergence-to-Go

In reinforcement learning, exploration is typically conducted by taking occasional random actions. The literature lacks an exploration method driven by uncertainty, in which exploratory actions explicitly seek to improve the learning process in a sequential decision problem. In this paper, we propose a framework called Divergence-to-Go, which is a model-based method that uses recursion similarly to dynamic programming to quantify the uncertainty associated with each state-action pair. Information-theoretic estimators of uncertainty allow our method to function even in large, continuous spaces. The performance is demonstrated on a maze and mountain car task.
Recursively Adapted Radial Basis Function Networks and its Relationship to Resource Allocating Networks and Online Kernel Learning

This paper proposes a recursively adapted radial basis function network and provides additional insights into several well-known techniques such as radial basis function networks, resource allocating networks and stochastic gradient descent in reproducing kernel Hilbert spaces. Through this perspective, resource allocating networks are investigated in a more principled way so that issues of convergence and generalization can be mathematically analyzed in the least mean square framework.
A reproducing kernel Hilbert space formulation of the principle of relevant information

Information theory allows one to pose problems in principled terms that very often have direct interpretation. For instance, capturing the structure based on statistical regularities of data can be thought of as a problem of relevance determination, that is, information preservation under limited resources. The principle of relevant information is an information theoretic objective function that attempts to capture the statistical regularities through entropy minimization under an information preservation constraint. Here, we employ an information theoretic reproducing kernel Hilbert space (RKHS) formulation, which can overcome some of the limitations of previous approaches based on Parzen density estimation. Results are competitive with kernel-based feature extractors such as kernel PCA. Moreover, the proposed framework goes further on the relation between information theoretic learning, kernel methods and support vector algorithms.
Feature extraction using information-theoretic learning

A classification system typically consists of both a feature extractor (preprocessor) and a classifier. These two components can be trained either independently or simultaneously. The former option has an implementation advantage since the extractor need only be trained once for use with any classifier, whereas the latter has an advantage since it can be used to minimize classification error directly. Certain criteria, such as minimum classification error, are better suited for simultaneous training, whereas other criteria, such as mutual information, are amenable for training the feature extractor either independently or simultaneously. Herein, an information-theoretic criterion is introduced and is evaluated for training the extractor independently of the classifier. The proposed method uses nonparametric estimation of Renyi's entropy to train the extractor by maximizing an approximation of the mutual information between the class labels and the output of the feature extractor. The evaluations show that the proposed method, even though it uses independent training, performs at least as well as three feature extraction methods that train the extractor and classifier simultaneously
An instrument for time synchronisation of data stored by unrelated equipment, in the clinical neurophysiology environment

Today neurophysiology laboratories take advantage from different, general purpose equipment, to store multimedia information pertinent to the ongoing exams . Along with patient's Electroencephalogram (EEG) written down on paper by means of multi-channel polygraphs generically known as EEG machines, it is also common to record the image of the patient throughout the exam, using commercial cameras and video tape recorders (VTR's). Computerised equipment, namely personal computers, are also getting more important every day within these environments; time or frequency domain analysis of the EEG, or support for cognitive tests, are currently performed using these machines.
A Novel Pitch Determination Algorithm based on Generalized Correlation Function

In this paper, we propose a novel pitch determination algorithm (PDA) based on the newly introduced concept of the generalized correlation function called correntropy . Correntropy contains a positive definite kernel function which nonlinearly transforms the original signal into a high dimensional reproducing kernel Hilbert space (RKHS), and calculates very efficiently the generalized correlation in that RKHS. By incorporating the kernel function, correntropy is able to utilize higher order statistics to enhance the resolution of pitch estimation. The proposed PDA calculates the summary of correntropy functions from the outputs of an equivalent rectangular bandwidth (ERB) filter bank. We present simulations on pitch determination for single vowel and double vowels cases. Simulations show that correntropy exhibits much better resolution than the conventional autocorrelation method for pitch determination.
Classification of hand movement direction based on EEG high-gamma activity

The Electroencephalogram (EEG) is a non-invasive technique used in the medical field to record and analyze brain activity. In particular, Brain Machine Interfaces (BMI) create this bridge between brain signals and the external world through prosthesis, visual interfaces and other physical devices. This paper investigates the relation between particular hand movement directions while using a BMI and the EEG recordings during such movement. The Common Spatial Pattern method (CSP) over the high-γ frequency band is utilized in order to discriminate opposite hand movement directions. The experiment is performed with three subjects and the average classification accuracy is obtained for two different cases.