nils-herrmann/clean_scirepeval huggingface dataset:
Learning an optimal decision strategy in an influence diagram with latent variables

We consider influence diagrams with one decision variable in which some variables can be unobservable. Such a diagram is learnable if, under some regularity conditions (“strict positivity” ), it is always possible to find, with probability 1 and from an infinitely large sample, an optimal strategy for choosing the value of the decision variable. We give a simple graphical criterion for determining whether an influence diagram is learnable and show that an optimal decision strategy for a learnable diagram can always be found by a straightforward method.  An znfltience diagram wtth latent uartables
Minimum description length estimators under the optimal coding scheme

Following Rissanen we consider the statistical model {P θ | as a code-book, θ indexing the codes. To obtain a single code, we first encode some θ and then encode our data x with the code corresponding to this θ. Rissanen's minimum description length principle recommends using the value of θ minimizing the total code length as an estimate of θ given the data x. For some standard statistical models we find easily computable estimators which respect this principle when θ is encoded with the asymptotically optimal coding scheme due to Levin and Chaitin.
A game of prediction with expert advice

We consider the following problem. At each point of discrete time the learner must make a prediction; he is given the predictions made by a pool of experts. Each prediction and the outcome, which is disclosed after the learner has made his prediction, determine the incurred loss. It is known that, under weak regularity, the learner can ensure that his cumulative loss never exceedscL+alnn, wherecandaare some constants,nis the size of the pool, andLis the cumulative loss incurred by the best expert in the pool. We find the set of those pairs (c, a) for which this is true.
Feature extraction for on-line EEG classification using principal components and linear discriminants

The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.
Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns

The paper describes work on the brain-computer interface (BCI). The BCI is designed to help patients with severe motor impairment (e.g. amyotropic lateral sclerosis) to communicate with their environment through wilful modification of their EEG. To establish such a communication channel, two major prerequisites have to be fulfilled: features that reliably describe several distinctive brain states have to be available, and these features must be classified on-line, i.e. on a single-trial basis. The prototype Graz BCI II, which is based on the distinction of three different types of EEG pattern, is described, and results of online and offline classification performance of four subjects are reported. The online results suggest that, in the best case, a classification accuracy of about 60% is reached after only three training sessions. The offline results show how selection of specific frequency bands influences the classification performance in singletrial data.
Dependence of coherence measurements on EEG derivation type

The impact is reported of different EEG derivation types on short-term changes in the inter-hemispheric coherence between the left and right sensorimotor areas, during the planning and execution of right index finger movements. Data are recorded during an event-related paradigm in which cued index finger movements are made. Event-related coherence analysis is then applied to the monopolar (nose reference) data, as well as source derivation. The results show that inter-hemispheric coherence between sensorimotor areas is dependent on the EEG derivation type. An increase in coherence during movement is found with nose reference and bipolar data, whereas for local average reference and source derivations, low inter-hemispheric coherence is observed, with no change in the coherence during movement. It is concluded that the coherence increase seen with nose, reference data is due to an indirecteeffect of mu rhythm desynchronisation, rather than any increase in synchrony of the mu rhythms themselves. Local average reference and source derivations better reflect the activity of the underlying cortical structures (the mu generating networks), and coherence analysis using these derivations shows that the mu rhythms of left and right hemispheres are not coherent.
Time-frequency microstructure of event-related electro-encephalogram desynchronisation and synchronisation

A new method is presented for the analysis of event-related EEG phenomena, in particular event related desynchronisation (ERD) and event related synchronisation (ERS) related to a voluntary movement; the method offers: high time-frequency resolution and, hence, increased ERD/ERS sensitivity (especially in the gamma band, where improvement can exceed an order of magnitude); the ability to analyse the whole picture of energy changes at once, without setting a priori the analysed frequency bands; and a parametric description of the signal's structures. The main idea is based upon averaging energy distributions of single EEG trials in the time-frequency plane. As the estimator for the signal's energy density, matching pursuit is chosen, with stochastic Gabor dictionaries. Other possible estimates are presented on a simulated signal and discussed briefly. The consistency of the results with previous findings is evaluated on the data from a classical voluntary finger movement experiment.
Brain death: Timing of apnea testing in primary brain stem lesion

In a 73-year-old patient complete areflexia of the cerebral and peripheral nerves following the rupture of an aneurysm of the basilar artery was diagnosed. During apnea testing the spectral analysis of electroencephalography (EEG) revealed an irreversible shift of peak from 6 to 3 Hz within the low frequency bands. These findings suggest that apnea testing in patients with primary lesion of the brain stem should be carried out only after an isoelectric EEG.
[Functional electrical stimulation instead of surgery? Improvement of grasping function with FES in a patient with C5 tetraplegia].

The aim of this study was to restore the grasp function of a tetraplegic patient with a C5 spinal cord injury (SCI) by means of functional electrical stimulation (FES). Using three pairs of surface electrodes and orthotic wrist stabilisation a simple palmar grasp was realised. The FES was controlled with a switch mounted on a wheelchair or-for the first time-with an EEG-based brain-computer interface (BCI). Application of this stimulation system enabled the patient to drink for the first time after the accident from a glass without any additional help.
Calculation of event-related coherence—A new method to study short-lasting coupling between brain areas

SummaryThis article deals with the estimation of event-related coherence (ERCoh) and its application to the planning and execution of self-paced index finger movement. ERCoh estimation complements the event-related desynchronization (ERD) measurements of rhythms within the alpha band. ERCoh yields information of the functional relationships between different brain areas as a function of time. The time resolution is 125 msec. Before movement onset a contralateral ERCoh increase was found between premotor and motor areas. This coherence increase was accompanied by an ERCoh decrease in parallel to the ERD over the contralateral centro-temporal areas. During movement, the ERD became bilaterally symmetrical. Simultaneously, interhemispheric coherence between contralateral and ipsilateral sensori-motor areas increased.
EEG-Based Asynchronous BCI Controls Functional Electrical Stimulation in a Tetraplegic Patient

The present study reports on the use of an EEG-based asynchronous (uncued, user-driven) brain-computer interface (BCI) for the control of functional electrical stimulation (FES). By the application of FES, noninvasive restoration of hand grasp function in a tetraplegic patient was achieved. The patient was able to induce bursts of beta oscillations by imagination of foot movement. These beta oscillations were recorded in a one EEG-channel configuration, bandpass filtered and squared. When this beta activity exceeded a predefined threshold, a trigger for the FES was generated. Whenever the trigger was detected, a subsequent switching of a grasp sequence composed of 4 phases occurred. The patient was able to grasp a glass with the paralyzed hand completely on his own without additional help or other technical aids.
Source localization using eventrelated desynchronization (ERD) within the alpha band

SummaryVoluntary finger movements result in a maximal ERD in the 10–12 Hz band close to electrodes C3 and C4, overlying the sensorimotor hand areas. This ERD focus is not very pronounced with EEG data recorded against a common reference electrode (monopolar recording). After transformation of the raw data using common average, local average and weighted average reference and the Hjorth method, respectively, the ERD becomes enhanced over electrodes C3 or C4. Movement-related EEG data were studied with 17, 19, 30 and 56 electrode montages using large and small interelectrode distances. The best focused ERD was obtained with a 56 electrode montage with small interelectrode distances and local average reference data.
Visual evoked potentials and electroretinography in brain-dead patients

Visual evoked potentials and electroretinograms were elicited by light-emitting diode stimulation and recorded simultaneously, with cephalic and noncephalic references, in 30 normal subjects and in 30 brain-dead patients. A characteristic pattern was found in the group of patients: when a cephalic reference was used for both visual evoked potentials and the electroretinogram, the a- and b-waves of the electroretinogram were recognized in all patients, and visual evoked responses consisted of waves with inverse polarity, similar morphologic characteristics, the same latency, and less amplitude than those of the electroretinogram. When a noncephalic derivation was chosen for the electroretinogram and visual evoked potentials, electroretinogram did not change in either morphologic features or latency, whereas the visual evoked potential channel showed no response. Only in two cases was it possible to record waves in the visual evoked potential lead with a noncephalic reference, showing a spread of the electroretinogram to the occipital area, with a considerably reduced amplitude. These results suggest that, although contamination of visual evoked potential records by the spread of the electroretinogram to the occipital area could occur, it is easy to confirm the absence of a true cortical visual response in brain-dead patients by means of a noncephalic reference. This pattern clearly confirms that in the visual pathways of brain-dead patients, electrical activity is confined to the retina.
Semiautomatisches Verfahren für die Untersuchung des synchronen Verhaltens von EEG- und Herzraten-Mustern bei Frühgeborenen / Semi-automatic procedure for the investigation of synchronized EEG- and heart rate patterns in preterm neonates

Recordings of the electroencephalogram (EEG) and of the heart rate variability (HRV) of preterm neonates can give important information on the actual state of the nervous system. Both signals, EEG and HRV, are affected by parameters such as gestational age, stage of maturation and behavioral state. This work describes a method for automatic detection of slow wave EEG-bursts and a tool to average changes in the EEG and the corresponding heart rate. The detection is based on the hjorth activity (HA), calculated from the EEG. HA spikes (HAS) are identified by the determination of the beginning and end of existing spikes. HAS maxima and the time between two consecutive HAS are the basis for the triggering of the bursts. EEG power and time synchronized HR changes are averaged with a time window length of 20 s. Resultant, HR increase and duration are determined. These parameters, obtained by the automatic detection, proved to be comparable to the results of an expert. Zusammenfassung Die kontinuierliche und simultane Aufzeichnung des Elektroenzephalogramms (EEG) und der Herzratenvariabilität (HRV) kann bei Risikofrühgeborenen wichtige Informationen über den Zustand des Nervensystems geben. Diese beiden Parameter werden unter anderem vom Gestationsalter, Reifegrad und Verhaltenszustand beeinflusst. In den vom Experten bestimmten Schlafphasen zeigen Frühgeborene ein diskontinuierliches „slow wave“-EEG-Muster. Das Ziel dieser Arbeit war, „bursts“ von „slow waves“ im EEG automatisch zu detektieren und eine Mittelung der EEG-Leistungsänderungen mit der dazugehörigen HR-Änderung bei den gefundenen „bursts” durchzuführen. Die Berechnungen für die Detektion basieren auf der synchron zum EEG berechneten Hjorth-Aktivität (HA). Durch die Bestimmung von Beginn und Ende der vorhandenen „spikes” werden die HA-„spikes“ (HAS) ermittelt. Die HAS-Maxima und die Zeiten zwischen zwei aufeinander folgenden HAS stellen die Grundlage für die Triggerung der „bursts“ dar. Die Mittelung der EEG-Leistung und der zeitlich synchronen HR-Änderung erfolgt mit einer Standardfensterbreite von 20 s mit einer automatischen Berechnung des HR-Anstieges sowie dessen Dauer. Es konnte gezeigt werden, dass die Wertebereiche dieser zwei Parameter, bei den Auswertungen durch den Experten mit den Ergebnissen der automatischen Detektion vergleichbar sind.
A Comparative Analysis of Multi-Class EEG Classification for Brain Computer Interface

Classifying different electroencephalogram (EEG) patterns is one of the key components to designing a usable Brain Computer Interface (BCI). Although it is well known that Support Vector Machine (SVM) is a strong classifier, it does not replace simple Linear Discriminant Analysis (LDA) or Nearest Neighbor Classifier (NNC), which are still in use in current BCI systems. This paper presents a comparison of SVM, LDA and NNC against each other by applying to the EEG data, where two methods were used to preprocess the raw EEG data: the Common Spatial Patterns (CSP) method and the feature extraction by estimate the adaptive autoregressive (AAR) parameters.
Propagation of EEG activity in the beta and gamma band during movement imagery in humans.

The objective of the paper was the determination of electrical brain activity propagation in sensorimotor areas during hand movement imagery.



Right-hand and left-hand movement imagination was studied in three subjects. The 10-channel Multivariate Autoregressive Model (MVAR) was fitted to EEG signals recorded from subsets of electrodes overlying central and related brain areas. By means of the Short-time Directed Transfer Function (SDTF) the propagation of brain activity as a function of frequency and time was found.



During imagery the relation between propagations in gamma and beta bands changed significantly for electrodes overlying sensorimotor areas, namely the increase in gamma was accompanied by the decrease in the beta band.



The hypothesis was put forward that these kinds of changes in flow of electrical brain activity are connected with the specific information processing.
Effects of a fast cable car ascent on the autonomic and central nervous system assessed by EEG and ECG analysis

In this study, ten healthy subjects performed a reaction time task at 990 m and 2700 m in altitude. The subjects were instructed to perform a right hand index finger movement as fast as possible after a green light flashed (repeated 50 times). The corresponding electrocardiogram (ECG) and the electroencephalogram (EEG) were recorded. From the ECG heart rate and heart rate variability measures in the time and frequency domain were calculated. An event-related desynchronization/synchronization (ERD/ERS) analysis was performed with the EEG data. Finally, the EEG activity and the ECG parameters were correlated. The study showed that with the fast ascent to 2700 m the heart rate increased and the heart rate variability measures decreased. Furthermore it was shown for the first time that the beta ERS in the 14 to 18 Hz frequency range (post-movement beta ERS) was significantly reduced at high altitude. Very interesting also is the loss of correlation between EEG activity and cardiovascular measures during finger movement at high altitude
Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.
Topographical display and interpretation of event-related desynchronization during a visual-verbal task

SummaryMultichannel EEG recordings were performed during a visual-verbal judgement task with a verbal response using an event-related paradigm. EEG trials of 7 sec were first digitally band pass filtered (10–12 Hz, 10–11 Hz, 11–12 Hz), and the amplitudes were then squared and averaged over all trials. This processing method results in a time course of alpha power and allows us to quantify the event-related desynchronization (ERD) in each EEG channel and to compute series of ERD maps in intervals of 125 msec. Analyzing the 10–11 Hz band, it was possible to study the time course and topographical pattern of cortical activation during visual encoding and cognition. Analysis of the 11–12 Hz band has enabled us, for the first time, to study the time course, spatial localization and extent of activation of speech, premotor and motor areas. We found that the speech centers were activated maximally 250 to 375 msec before speech onset and the SMA about 250 msec before speech onset. The results are preliminary, but demonstrate how much information can be extracted from the scalp EEG.
Characterization of four-class motor imagery EEG data for the BCI-competition 2005.

To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.
Phasic heart rate response in virtual environments

Heart rate responses induced by motor imagery were investigated in 4 subjects in a series of experiments with a Brain-Computer Interface (BCI). The goal of the BCI experiment was either to control a bar on a PC monitor or to move forward within a virtual environment (VE). In the first case all subjects displayed a HR decrease during motor imagery in the order of 2 – 5 %. The thought-based control of VE resulted in a heart rate increase in 2 subjects and a heart rate decrease in the other 2 subjects. The heart rate acceleration in the VE is interpreted as effect of mental effort and motivation.
Spatiotemporal analysis of alpha frequency components with the ERD technique

SummaryEEG data from 30 channels were recorded during movement and reading tasks and analyzed in the three frequency bands 6– 8 Hz, 8–10 Hz and 10–12 Hz. For each frequency band, the ERD (event-related desynchronization) was quantified and displayed in the form of time courses and maps. The results show that the ERD pattern varies with the frequency component analyzed. In general, upper alpha components (10–12 Hz) demonstrate a short-lasting, task-specific and localized ERD; the ERD of lower alpha components is long-lasting (> 1 sec) and widespread. The ERD can be interpreted as a sign of cortical activation, whereby desynchronization of upper alpha components may reflect more task-specific processes, and desynchronization of middle and lower alpha components may be related to the level of expectancy and attention.
Walking from thoughts: Not the muscles are crucial, but the brain waves!

Able-bodied participants are able to move forward in a Virtual Environment (VE) by imagining movements of their feet. This is achieved by exploiting a Brain-Computer Interface (BCI) which transforms thought-modulated EEG signals into an output signal that controls events within the VE. The experiments were carried out in an immersive projection environment, commonly referred to as a "Cave” in which participants were able to move through a virtual street by foot imagery alone. Experiments of BCI feedback on a normal monitor, VE experiments with a head-mounted display (HMD) and in the Cave-VE are compared.
[Semiautomatic procedure for the investigation of synchronized activity of EEG and heart rate--examination of preterm births].

Recordings of the electroencephalogram (EEG) and of the heart rate variability (HRV) of preterm neonates can give important information on the actual state of the nervous system. Both signals, EEG and HRV, are affected by parameters such as gestational age, stage of maturation and behavioral state. This work describes a method for automatic detection of slow wave EEG-bursts and a tool to average changes in the EEG and the corresponding heart rate. The detection is based on the hjorth activity (HA), calculated from the EEG. HA spikes (HAS) are identified by the determination of the beginning and end of existing spikes. HAS maxima and the time between two consecutive HAS are the basis for the triggering of the bursts. EEG power and time synchronized HR changes are averaged with a time window length of 20 s. Resultant, HR increase and duration are determined. These parameters, obtained by the automatic detection, proved to be comparable to the results of an expert.