nils-herrmann/clean_scirepeval huggingface dataset:
Elimination von Atmungseffekten auf bewegungsinduzierte Änderungen der Herzrate - Elimination of Respiratory Effects on Movement-induced Cardiac Response

: As part of a comprehensive movement study, the cardiac response induced by voluntary movement of the right and left index fingers was analysed. Since subjects synchronised movement and respiration, a special method that permitted the differentiation of movement-induced from respiratory-related effects was employed. This method is described, and its advantages over the standard method of estimating stimulus-induced cardiac responses are discussed. The results obtained in three subjects are presented, and the impact of the synchronisation of movement and respiration on the induced cardiac response demonstrated.
Cortical Imaging Based on an Analytic High Resolution EEG

It is well known that the EEG is a blurred and spatially low-pass filtered representation of the cortical activity. Since additional recording electrodes will not necessarily lead to an improved spatial resolution, further steps have to be taken. We derived the analytic downward continuation of the scalp potential field to an arbitrary inner surface for a spherical volume conductor model with piecewise constant conductivities. The basic idea of the Analytic High Resolution BEG (AHREEG) is the fact that a function defined on a sphere can be expressed as a weighted sum of spherical harmonics. Considering the spatial transfer function between the cortex and the scalp surface, the potential distribution on the cortex is theoretically computed by the application of the inverse transfer function to the scalp potential field. Compared to source localization procedures, the AHREEG is not based on any source distribution or on the nature of the sources. Furthermore the proposed method is unique, though ill-posed. Due to this fact and the fact that real world data are always contaminated by noise, a regularization based on general cross validation is performed to stabilize the inverse solution. Simulation results as well as the application of the AHREEG to EEG recorded during median nerve stimulation will be presented and critically discussed.
SUBJECT SPECIFIC EEG PATTERNS DURING MOTOR IMAGINARY

2  Left and right hand movement imagery is accompanied by an EEG event-related desynchronization (ERD) over the contralateral hand area. The time courses of the ERD from two brain-computer interface experiments were investigated by the calculation of instantaneous band power changes and by adaptive autoregressive model parameters combined with linear discriminant analysis. Subject specific differences of the EEG reactivity patterns were observed.
Time/space regularization of the inward continuation problem in EEG using the Boundary Element Method

The inward continuation problem in EEG consists in the computation of the cortical potential distribution from the measured potential distribution on the scalp. Although unique this inverse problem is ill-posed. That is, low-level noise in the scalp potential data or a small error in the geometrical data can lead to unbounded errors in the solution. Regularization techniques have to be used to minimize these effects. The inverse problem is solved in two steps. First Tikhonov regularization is applied yielding a solution of the potential on the inside of the skull surface for every timestep. Than the solution of the first step is used for Twomey regularization. At each moment in time a new solution is found by using as a priori estimate the average of the first solution one timestep prior and one timestep after. This combination of spatial (Tikhonov) and temporal (Twomey) regularization improves the solution and smoothes the solution in space and time. Both simulations and the application to EEG data of a Median Nerve stimulation experiment yield encouraging results. Further comparative studies have to be carried out to evaluate the application of time/space regularization of the inward continuation problem in EEG.
[Elimination of respiratory effects on motion-induced changes in heart rate].

As part of a comprehensive movement study, the cardiac response induced by voluntary movement of the right and left index fingers was analysed. Since subjects synchronised movement and respiration, a special method that permitted the differentiation of movement-induced from respiratory-related effects was employed. This method is described, and its advantages over the standard method of estimating stimulus-induced cardiac responses are discussed. The results obtained in three subjects are presented, and the impact of the synchronisation of movement and respiration on the induced cardiac response demonstrated.
Effects of handedness on movement-related changes of central beta rhythms.

The effects of handedness on the movement-related changes in beta rhythms (14-30 Hz) in the left and right perirolandic area were analyzed in 12 right-handed and 11 left-handed subjects. The motor task consisted of unilateral brisk or slow self-paced extension of the right or left index finger. The handedness effects were as follows. First, in both handedness groups, the premovement desynchronization of beta rhythms at both hemispheres was greatest before slow movement of the "nondominant" finger, especially at electrodes presumably overlying the MI areas. Second, the lefthanded group showed less desynchronization in both hemispheres during execution of a slow movement than the righthanded group. Third, the postmovement beta synchronization showed a contralateral preponderance which was greater after movements of the nondominant than the "dominant" finger in the righthanded group and was equal for both fingers in the lefthanded group. The results suggest that handedness effects on movement-related changes in central beta rhythms are coupled to movements of the nondominant finger and that their manifestation differs in the pre- and postmovement periods.
Mu‐rhythm changes in brisk and slow self‐paced finger movements

WE analysed whether type of movement (brisk vs slow) and active muscle force are encoded in the time course of mu-rhythm desynchronization during self-paced finger movements. Ten subjects performed 100 brisk and slow extensions of the right index finger. The time course of mu-rhythm desynchronization in the contralateral sensorimotor area before movement was identical for both types of movements. Brisk movements accompanied by a stronger extensor muscle contraction were preceded by larger desynchronization. The onset of mu-rhythm recovery was related to the duration of the extensor EMG burst in both types of movement. The results suggest that both amplitude and duration of the extensor muscle contraction are encoded in the time course of mu-rhythm desynchronization.
Brainstem auditory evoked potentials in respiratory insufficiency following encephalitis.

Brainstem auditory evoked potentials (BAEPs) were recorded in 14 artificially ventilated patients (12 males, 2 females; mean age 33.3 +/- 16.3 years, range 18-67) with respiratory insufficiency resulting from severe inflammatory encephalopathies. The results were compared with those of 17 healthy volunteers (13 males, 4 females; mean age 27.4 +/- 5.3 years, range 21-45). BAEPs in the study patients showed prolonged interpeak latencies (I-III, I-V, III-V, IV-V) and delayed absolute latencies of waves I, II, III, and V at least on one side. Because the auditory pathways are in the near vicinity of the respiratory control centers in the brainstem, the electrophysiologic abnormalities of wave III and the IV/V complex may be a reflection of the disturbed central control of ventilation.
EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.

Several laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.
Second order and third order statistics with application to EEG [finger movements]

Linear and non-linear phase relationships between EEG signals, measurement different electrode positions were analysed. Using second and third order statistics relations between cortical areas during preparation and execution of finger movements were investigated. Interrelations between left and right primary hand areas and between left hand area and supplementary motor area were computed. For linear analysis coherence and for non-linear interrelations crossbicoherence was used. Intrasignal relations were quantified with the bicoherence.
Distinction Sensitive Learning Vector Quantization (DSLVQ) application as a classifier based feature selection method for a Brain Computer Interface

This paper describes a simple but very powerful method for feature selection. The Distinction Sensitive Learning Vector Quantizer (DSLVQ) is a learning classifier which focuses on relevant features according to its own instance based classifications. Two different experiments describe the application of DSLVQ as a feature selector for an EEG-based Brain Computer Interface (BCI) system. It is shown that optimal electrode positions as well as frequency bands are strongly dependent on each subject and that a subject specific feature selection is when important for BCI systems.
Realtime Measurement of Stroke Volume -An Adaptive Monitoring System and Its Application - Echtzeitbestimmung des Schlagvolumens - Ein adaptives Monitoring- System und seine Anwendung im Bereich der Kreislaufforschung

A variety of monitoring systems is available for special fields of clinical application and scientific research. Whenever requirements change data acquisition and handling of signal analysis have to be adapted or even completely new structured, especially in real-time applications. This can be a time consuming procedure. In order to be able to focus on the development of algorithms without taking care how data are acquired a monitoring system has been developed which can be adjusted in terms of signal acquisition, analysis and user handling. New algorithms can be added and polygraphical presentation of results can be adjusted for special clinical requirements. As a specific application the online and real time calculation left ventricular ejection time (LVEJT), stroke volume (SV), cardiac output (CO), mean blood pressure and total peripheral resistance (TPRI) is presented.
Dynamic cross-spectral analysis of event-related EEG using ensemble averages

A method of investigating the dynamic cross-spectral properties of event-related EEG is reported. A sequence of multivariate autoregressive models is fitted to segments of the EEG within which the data exhibit local stationarity. For estimation a procedure involving ensemble averages is presented. Time courses of autospectra and coherence are derived from the multivariate models. The method is applied to event-related EEG recorded from primary and supplementary motor areas during externally triggered finger movements.
Event-Related Desynchronization and Synchronization of Mu and Beta Rhythms in Brisk and Slow Self-Paced Finger Movements

Desynchronization of muand beta-band rhytbms precedes a voluntary movement by more than l seconds (Pfurtscheller and Aranibar 1979; Pfurtscheller 1981; Pfurtscheller and Bergbold 1989). Since mu· and beta-band ERD reflects the process of motor planning and programming, a cballenging quesüon bas been posed whether distinct motor qualities, i.e. muscle force, movement duration, velocity etc. are also encoded in the lime course of muand beta-band ERD. Earlier studies jhave shown that voluntary movements are basically of i two types, i.e. the ballistic (brisk) and the ramp (slow controlled) type (Wachholder and Altenburger 1926). In addition, planning of slow and brisk movements seems to involve different brain structures with more engagement of basal ganglia in slow and cerebellum in j brisk movements (Komhuber 1971; DeLong and Strick 1974). Besides desynchronization of muand beta-band rhythms, their recovery or event-related synchronization (ERS) has attracted attention (Pfurtscheller 1981; Pfurtscheller 1992). One recent neuromagnetic study (Salmelin and Hari 1994) showed that the source of the beta-band recovery after self-paced thumb movement is located in the precentral gyrus whereas the source corresponding to muand beta-band desynchronization was located more posterior. Our recent EEG study showed that beta-band recovery after a self-paced finger movement started in the precentral MI area, and ofien another focus has been observed in the precentral medial cortex (Stancäk Jr. and Pfurtscheller, in press). The present study was aimed at the comparison of muand beta-band desynchronization and recovery in brisk and slow movements, and at the analysis of ERD profiles with respect to the force of contraction in the extensor muscle accompanying movement
Internal Regret in On-Line Portfolio Selection

This paper extends the game-theoretic notion of internal regret to the case of on-line potfolio selection problems. New sequential investment strategies are designed to minimize the cumulative internal regret for all possible market behaviors. Some of the introduced strategies, apart from achieving a small internal regret, achieve an accumulated wealth almost as large as that of the best constantly rebalanced portfolio. It is argued that the low-internal-regret property is related to stability and experiments on real stock exchange data demonstrate that the new strategies achieve better returns compared to some known algorithms.
Incomplete information and internal regret in prediction of individual sequences

This thesis takes place within the theory of prediction of individual sequences. The latter avoids any modelling of the data and aims at providing some techniques of robust prediction and discuss their possibilities, limitations, and difficulties. It considers issues arising from the machine learning as well as from the game-theory communities, and these are dealt with thanks to statistical techniques, including martingale concentration inequalities and minimax lower bound techniques. The obtained results consist, among others, in external and internal regret minimizing strategies for label efficient prediction or in games with partial monitoring. Such strategies are valuable for the on-line pricing problem or for on-line bandwidth allocation. We then focus on internal regret for general convex losses. We consider first the case of on-line portfolio selection, for which simulations on real data are provided, and generalize later the results to show how players can learn correlated equilibria in games with compact sets of strategies.
Data-Dependent Analysis of Learning Algorithms

This thesis studies the generalization ability of machine learning algorithms in a statistical setting. It focuses on the data-dependent analysis of the generalization performance of learning algorithms in order to make full use of the potential of the actual training sample from which these algorithms learn. First, we propose an extension of the standard framework for the derivation of generalization bounds for algorithms taking their hypotheses from random classes of functions. This approach is motivated by the fact that the function produced by a learning algorithm based on a random sample of data depends on this sample and is therefore a random function. Such an approach avoids the detour of the worst-case uniform bounds as done in the standard approach. We show that the mechanism which allows one to obtain generalization bounds for random classes in our framework is based on a “small complexity” of certain random coordinate projections. We demonstrate how this notion of complexity relates to learnability and how one can explore geometric properties of these projections in order to derive estimates of rates of convergence and good confidence interval estimates for the expected risk. We then demonstrate the generality of our new approach by presenting a range of examples, among them the algorithm-dependent compression schemes and the data-dependent luckiness frameworks, which fall into our random subclass framework. Second, we study in more detail generalization bounds for a specific algorithm which is of central importance in learning theory, namely the Empirical Risk Minimization algorithm (ERM). Recent results show that one can significantly improve the highprobability estimates for the convergence rates for empirical minimizers by a direct analysis of the ERM algorithm. These results are based on a new localized notion of complexity of subsets of hypothesis functions with identical expected errors and are therefore dependent on the underlying unknown distribution. We investigate the extent to which one can estimate these high-probability convergence rates in a datadependent manner. We provide an algorithm which computes a data-dependent upper bound for the expected error of empirical minimizers in terms of the “complexity” of data-dependent local subsets. These subsets are sets of functions of empirical errors of a given range and can be determined based solely on empirical data. We then show that recent direct estimates, which are essentially sharp estimates on the highprobability convergence rate for the ERM algorithm, can not be recovered universally from empirical data.
Optimal Sample-Based Estimates of the Expectation of the Empirical Minimizer

We study sample-based estimates of the expectation of the function produced by the empirical minimization algorithm. We investigate the extent to which one can estimate the rate of convergence of the empirical minimizer in a data dependent manner. We establish three main results. First, we provide an algorithm that upper bounds the expectation of the empirical minimizer in a completely data-dependent manner. This bound is based on a structural result in , which relates expectations to sample averages. Second, we show that these structural
Local Complexities for Empirical Risk Minimization

We present sharp bounds on the risk of the empirical minimization algorithm under mild assumptions on the class. We introduce the notion of isomorphic coordinate projections and show that this leads to a sharper error bound than the best previously known. The quantity which governs this bound on the empirical minimizer is the largest fixed point of the function \(\xi_{n}(r)=\mathbb{E}{\rm sup}\{|\mathbb{E}f-\mathbb{E}_{n}f|:f\in F,\mathbb{E}f=r\}\). We prove that this is the best estimate one can obtain using “structural results”, and that it is possible to estimate the error rate from data. We then prove that the bound on the empirical minimization algorithm can be improved further by a direct analysis, and that the correct error rate is the maximizer of ξ′ n (r) − r, where \(\xi'_{n}(r)=\mathbb{E}{\rm sup}\{\mathbb{E}f-\mathbb{E}_{n}f:f\in F,\mathbb{E}f=r\}\).
On the Importance of Small Coordinate Projections

It has been recently shown that sharp generalization bounds can be obtained when the function class from which the algorithm chooses its hypotheses is "small" in the sense that the Rademacher averages of this function class are small. We show that a new more general principle guarantees good generalization bounds. The new principle requires that random coordinate projections of the function class evaluated on random samples are "small" with high probability and that the random class of functions allows symmetrization. As an example, we prove that this geometric property of the function class is exactly the reason why the two lately proposed frameworks, the luckiness (Shawe-Taylor et al., 1998) and the algorithmic luckiness (Herbrich and Williamson, 2002), can be used to establish generalization bounds.
Random Subclass Bounds

It has been recently shown that sharp generalization bounds can be obtained when the function class from which the algorithm choo-ses its hypotheses is “small” in the sense that the Rademacher averages of this function class are small . Seemingly based on different arguments, generalization bounds were obtained in the compression scheme , luckiness [13], and algorithmic luckiness [6] frameworks in which the “size” of the function class is not specified a priori.
Facial Recognition Vendor Test 2000

s recently as 1993, facial recognition was thought of as a technology that would probably never work, and if it did, it would have limited use in the real world. The Department of Defense (DOD) Counterdrug Technology Development Program Office's Face Recognition Technology Program (FERET) changed that opinion through numerous development efforts, data collection and evaluations. When FERET was completed in 1997, the algorithms that were merely in developers' minds four years earlier were being transitioned to the marketplace. Today, there are dozens of facial recognition systems available that have the potential to meet performance requirements for numerous applications. But which of these systems best meet the performance requirements for given applications? Repeated inquiries from numerous government agencies on the current state of facial recognition technology prompted the DOD Counterdrug Technology Development Program Office to establish a new set of evaluations. The Facial Recognition Vendor Test 2000 (FRVT 2000) was co-sponsored by the DOD Counterdrug Technology Development Program Office, the National Institute of Justice (NIJ) and the Defense Advanced Research Projects Agency, and was administered last spring. This article provides an overview of FRVT 2000 and discusses follow-up efforts that will evaluate the feasibility of facial recognition in a correctional environment.
JADETD : COMBINING HIGHER-ORDER STATISTICS AND TEMPORALINFORMATION FOR BLIND SOURCE SEPARATION ( WITH NOISE )

So far blind separation algorithms have been either using information from higher order statistics or from time structure in the data. We propose the use of an error function that merges both types of information and obtain the JADE T D algorithm. This way we combine the virtues of both ingredients: the TDSEP and the JADE algorithm. We investigate the performance and usefulness of the new algorithm in several experimental scenarios. A particularly interesting scenario is the robustness of blind separation in the presence of noise, where the measuring noise eeectively doubles the number of sources and therefore leads to large separation errors for blind separation algorithms. To still allow for separation in the presence of noise, we propose to use time delayed covariance matrices for the whitening step and for the estimation of the subsequent rotation step, accordingly, also delayed cumulant information is taken into account. This technique makes use of the fact that the autocorrelation function (ACF) of the measuring noise decays much faster than the auto-correlation function of the signals.