nils-herrmann/clean_scirepeval huggingface dataset:
CARDIAC FIELD ARTIFACT IN SLEEP EEG

Estimates of the cardiac field artifact were obtained for each EEG/EOG channel of all-night sleep EEG/ECG recordings by heartbeat-related averaging of the EEG. These estimates – so-called templates – can be used for minimization of the art ifact, e. g. by heartbeat-synchronized subtraction o f the template. The 3-dimensional nature of the cardiac field artifact is demonstrated by constructing vectorcardiograms from the templates and the corresponding electrode positions.
Event-Related changes of band power and coherence: methodology and interpretation.

Event-related calculation of band power changes can be used to quantify event-related desynchronization, event-related synchronization, and event-related coherence (ERCoh). It is shown that in the case of a motor task especially, the ERCoh time course depends on the type of EEG derivation used, whereby referenced EEG data can result in a bilateral coherence increase, although both hemispheres generate independent sensorimotor rhythms. It is further shown that not only Rolandic mu rhythms but also central beta rhythms display a lack of interhemispheric linear phase coupling.
Cortical oscillatory activity assessed by combined EEG and MEG recordings and High-Resolution ERD methods

Event-related desynchronization is a phenomenon of the awake brain and was quantified for the first time by Pfurtscheller and Aranibar . ERD of alpha band rhythms can be seen äs a correlate of activated cortical neurons and was reported during sensory, motor and cognitive processing. In evoked potential/field studies the responses of the brain activity with respect to the Stimuli are phase locked to the event. In order to improve the signal-tonoise ratio (SNR) trials of EEG/MEG recordings are averaged with respect to the event. Source localization procedures or cortical imaging methods are then applied to the averaged potentials/fields. However, in event-related desynchroni-zation studies the response of the brain (i.e. the induced rhythmic activity) to the Stimuli is not phase locked to the event. Therefore, simple amplitude a veraging of trials with respect to the event can not be done properly to improve the SNR. To overcome this problem the non-stationary EventRelated Desynchronization analysis (ERD) was introduced . The outcome of the ERD analysis resembles in some way an averaged evoked response signal with a temporal profile depending on the mean power ratio between the EEG in the actual measurement interval and a reference interval. It is assumed that most of the ongoing EEG observed on the scalp (by EEG recordings) or outside the head (by MEG recordings) is due to superficially located neo-cortical sources [2],[3]. Dipole layers can describe the sources. Based on fmdings by  mainly radially orientated dipole-layers (with respect to the cortical surface) contribute to the observed ERD/ERS. Combined EEG and MEG recordings have (at least theoretically) higher Information content for identifying brain source activity than single EEG and MEG recordings . In order to investigate the usefulness of simultaneous EEG and MEG recordings the topographical differences in EEG and/or MEG maps based on simple self-paced finger movements are investigated. High-resolution ERD methods based on a distributed cortical source space [6] combining biomagnetic and bioelectric data are applied to the acquired data sets.
Human Movement-Related Potentials vs Desynchronization of EEG Alpha Rhythm: A High-Resolution EEG Study

Movement-related potentials (MRPs) and event-related desynchronization (ERD) of alpha rhythm were investigated with an advanced high-resolution electroencephalographic technology (128 channels, surface Laplacian estimate, realistic head modeling). The working hypothesis was that MRPs and alpha ERD reflect different aspects of sensorimotor cortical processes. Both MRPs and alpha ERD modeled the responses of primary sensorimotor (M1-S1), supplementary motor (SMA), and posterior parietal (PP, area 5) areas during the preparation and execution of unilateral finger movements. Maximum responses were modeled in the contralateral M1-S1 during both preparation and execution of the movement. The SMA and PP responses were modeled mainly from the MRPs and alpha ERD, respectively. The modeled ipsilateral M1-S1 responses were larger and stronger in the alpha ERD than MRPs. These results may suggest that alpha ERD reflects changes in the background oscillatory activity in wide cortical sensorimotor areas, whereas MRPs represent mainly increased, task-specific responses of SMA and contralateral M1-S1.
EEG-based Brain-Computer Interface Using Subject-Specific Spatial Filters

Sensorimotor EEG rhythms are affected by motor imagery and can, therefore, be used as input signals for an EEG-based brain-computer interface (BCI). Satisfactory classification rates of imagery-related EEG patterns can be activated when multiple EEG recordings and the method of common spatial patterns is used for parameter estimation. Data from 3 BCI experiments with and without feedback are reported.
Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery.

EEG feedback studies demonstrate that human subjects can learn to regulate electrocortical activity over the sensorimotor cortex. Such self-induced EEG changes could serve as control signals for a Brain Computer Interface. The experimental task of the current study was to imagine either right-hand or left-hand movement depending on a visual cue stimulus on a computer monitor. The performance of this imagination task was controlled on-line by means of a feedback bar that represented the current EEG pattern. EEG signals recorded from left and right central recording sites were used for on-line classification. For the estimation of EEG parameters, an adaptive autoregressive model was applied, and a linear discriminant classifier was used to discriminate between EEG patterns associated with left and right motor imagery. Four trained subjects reached 85% to 95% classification accuracy in the course of the experimental sessions. To investigate the impact of continuous feedback presentation, time courses of band power changes were computed for subject-specific frequency bands. The EEG data revealed a significant event-related desynchronization over the contralateral central area in all subjects. Two subjects simultaneously displayed synchronization of EEG activity (event-related synchronization) over the ipsilateral side. During feedback presentation the event-related desynchronization/event-related synchronization patterns showed increased hemispheric asymmetry compared to initial control sessions without feedback.
Artifact processing of the sleep EEG in the "SIESTA"-project

Artefact processing is an important issue in automated analysis of the sleep EEG. Nine artefact types were identified and their frequency of occurrence was analysed in 15 all-night sleep recordings from eight European sleep laboratories. Three artefact detection methods were evaluated with the area-under-ROC-curve (AUC), eight artefact minimisation methods were validated visually. Recommended methods are overflow detection, adaptive inverse filtering for movement and muscle detection, adaptive FIR filtering for ECG minimisation and 50Hz notch filtering.
GDF - A GENERAL DATAFORMAT FOR BIOSIGNALS VERSION 1.25

A magnetic reed switch assembly for activating an electromagnetic grapple utilized to hold a control rod in position above a reactor core. In normal operation the magnetic field of a permanent magnet is short-circuited by a magnetic shunt, diverting the magnetic field away from the reed switch. The magnetic shunt is made of a material having a Curie-point at the desired release temperature. Above that temperature the material loses its ferromagnetic properties, and the magnetic path is diverted to the reed switch which closes and short-circuits the control circuit for the control rod electromagnetic grapple which allows the control rod to drop into the reactor core for controlling the reactivity of the core.
Functional imaging of postmovement beta event-related synchronization.

The linear estimation (LE) and spline surface Laplacian (SL) method were applied to single-trial EEG data. EEG was recorded in three subjects during voluntary, self-paced movements of the index finger. The EEG data were bandpass-filtered in the lower beta frequency range and showed short-lasting bursts of oscillations after termination of movement. These oscillations are termed postmovement beta synchronization. The realistic head geometry and the digitized positions of the electrodes were taken into account for accurate modeling of the anatomy. Regularization of the LE method was achieved by the truncated singular value decomposition. The LE and SL distribution of the postmovement beta synchronization showed similar spatial and temporal patterns. A clear increase of the LE source activity was found over the primary motor area. These results indicate that the postmovement beta synchronization is generated over the anterior bank of the central sulcus.
Frequency component selection for an EEG-based brain to computer interface.

A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number of different brain states. In a subsequent series of feedback sessions, where the subject is confronted with the classification results, the subject tries to reduce the number of misclassifications. In this study the relevance of different spectral components is analyzed: 1) on the training sessions to select optimal frequency bands for the feedback sessions and 2) on the feedback sessions to monitor changes.
High-resolution ERD: cortical imaging of event-related desynchronization during motor imagery

The objective of this paper is to combine spatially enhanced event-related desynchronization (ERD) data recorded during motor imagery experiments to the anatomy of the brain. Four methods available, the local average reference method (LAR), the surface Laplacian method (LP), the linear estimation method (LE) and the analytical de-blurring method (AD) are applied to the event-related data. All methods yield similar and stable ERD patterns with respect to the location of the maximal desynchronization. The maximal ERD percentage values were found over the contralateral sensorimotor areas of the brain around 625 ms after the presentation of the cue stimulus.
High-Resolution ERD Using Realistic Head Geometry

Event-related desynchronization (ERD) of Rolandic mu and beta rhythms is characteristic for a voluntary movement . The ERD Starts about 2 s before the actual movement over the contralateral hemisphere and gets bilaterally symmetrical with movement execution . Movement-related magnetic fields measurements showed that the mu-rhythm is mostly of somatosensory origin whereas the beta activity seems to be generated in the primary motor cortex [3). Imagination of movement results in an ERD over the sensorimotor area [4], The aim of this paper is to compare two different highresolution methods, that is the surface Laplacian (SL) and the Linear Estimation (LE) method. The realistic geometry of the subject's head was taken into account. We investigated data recorded during motor imagery of right and left hand movements, respectively.
Postmovement beta synchronization in patients with Parkinson's disease.

Event-related synchronization (ERS) after self-paced, voluntary brisk movement of the right and left thumb was studied in 17 patients with Parkinson's disease (PD) and 17 age-matched control subjects. All patients were receiving L-DOPA and/or DOPA-agonists. The movement-offset-triggered EEG data were analyzed in the 12- to 16-Hz, 16- to 20-Hz, and 20- to 24-Hz bands for eight time intervals after termination of movement. Significant differences in postmovement beta synchronization were observed in all three frequency bands. As compared with the control group, patients with PD showed a remarkably smaller beta ERS. This was the overall main effect for groups, as well as for interactions concerning side of movement and electrode positions. If beta ERS is a measure of recovery of the primary motor area after movement, our results indicate that this ability is impaired in PD patients.
On the realization of an analytic high-resolution EEG

The analytic solution of the harmonic downward continuation of the scalp potential field in an N-shell heterogeneous, but isotropic, spherical volume conductor model has been derived. The objective of this paper was to investigate the realization of a so-called "high-resolution electroencephalogram (EEG)": by enhancing the poor spatial resolution of EEG recordings. To this end, the forward problem for a dipolar source arbitrarily located at the source point Q=Q(r/sub s/,/spl phi//sub s/,/spl thetav//sub s/) has been determined in a compact matrix notation. It is possible to transfer the potential field given on the outer surface of a spherically shaped volume conductor to an arbitrary inner surface (e.g., to the cortical surface) under consideration of the electrical and geometrical properties of the model. For the application of the proposed method to real-world problems, the coefficients of the series expansion describing the cortical potential distribution are determined by minimizing the squared curvature of the scalp potential field integrated over the scalp surface. Simulation results for distributed sources show that the proposed method is superior to the surface Laplacian method for interelectrode distances below 2.5 cm.
Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters.

Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.
EEG-based communication: improved accuracy by response verification.

Humans can learn to control the amplitude of electroencephalographic (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could provide a new augmentative communication channel for individuals with motor disabilities. In the present system, each dimension of cursor movement is controlled by a linear equation. While the intercept in the equation is continually updated, it does not perfectly eliminate the impact of spontaneous variations in EEG amplitude. This imperfection reduces the accuracy of cursor movement. We evaluated a response verification (RV) procedure in which each outcome is determined by two opposite trials (e.g., one top-target trial and one bottom-target trial). Success, or failure, on both is required for a definitive outcome. The RV procedure reduces errors due to imperfection in intercept selection. Accuracy for opposite-trial pairs exceeds that predicted from the accuracies of individual trials, and greatly exceeds that for same-trial pairs. The RV procedure should be particularly valuable when the first trial has >2 possible targets, because the second trial need only confirm or deny the outcome of the first, and it should be applicable to nonlinear as well as to linear algorithms.
Subject specific EEG patterns during motor imaginary [sic.: for imaginary read imagery]

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.
SINGLE-TRIAL EEG ANALYSIS USING AN ADAPTIVE AUTOREGRESSIVE MODEL

An important aspect for the realization of a Brain-Computer Interface (BCI) is the analysis of spatio-temporal EEG patterns. Features have to be extracted, which represent the state of the brain. In other words, the differences of the brain-state for different mental processes should be derived. In the research work presented here, the different processes are preparations for left and right finger movement. These should be represented in different brain states. Several effects make it difficult to identify differences of the brain-state. These are: • the Inter-trial variability, the brain state is build up by multiple processes, but only the process preparing the movement is of interest • Inter-individual variability, the brain processes between subjects and/or sessions are not identical. • Limitation of the method used for feature extraction, the evaluation methods have to be applied across trials and across several subjects; a method is superior to others if the error rate across different subjects is low.
EEG-Based Communication: Evaluation of Alternative Signal Prediction Methods - EEG-basierte Kommunikation: Evaluierung alternativer Methoden zur Signalprädiktion

Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. In the first two methods-moving average, and weighted sum-a single intercept was selected for the entire 1-2 sec period of each trial. In the other three methods-blocked moving average, blocked weighted sum, and blocked recursive sum (a variation of the weighted sum)-an intercept was selected for each 200-ms segment of the trial. The results from these methods were compared in regard to their balance between upward and downward movements and their consistency of performance across trials. For all subjects combined, the five methods performed similarly. However, performance across subjects was more consistent for the moving average, blocked moving average, and blocked recursive sum methods than for the weighted sum and blocked weighted sum methods. Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.
Timing of EEG-based cursor control.

Recent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control. This study examined the course of EEG control after presentation of a target. At the beginning of each trial, a target appeared at the top or bottom edge of the subject's video screen and 1 sec later a cursor began to move vertically as a function of EEG amplitude in a specific frequency band. In well-trained subjects, this amplitude was high at the time the target appeared and then either remained high (i.e., for a top target) or fell rapidly (i.e., for a bottom target). Target-specific EEG amplitude control began 0.5 sec after the target appeared and appeared to wax and wane with a period of approximately 1 sec until the cursor reached the target (i.e., a hit) or the opposite edge of the screen (i.e., a miss). Accuracy was 90% or greater for each subject. Top-target errors usually occurred later in the trial because of failure to reach and/or maintain sufficiently high amplitude, whereas bottom-target errors usually occurred immediately because of failure to reduce an initially high amplitude quickly enough. The results suggest modifications that could improve performance. These include lengthening the intertrial period, shortening the delay between target appearance and cursor movement, and including time within the trial as a variable in the equation that translates EEG into cursor movement.
Post-movement Beta synchronization studied with linear estimation

Event-related desynchronization (ERS) describes a short-lasting and localized amplitude enhancement of specific frequency components. The spatial distribution of a post-movement beta ERS can be visualized by computing the local average reference (LAR). The Linear estimation (LE) method can also be applied to study the spatiotemporal ERS patterns. As source space an hemisphere was used with equally distributed radially oriented current dipoles. The lead field matrix is normalized to make sure that all dipoles have the same average impact on the sensors. A distributed source solution is found for each timestep and for each trial. Event-related Desynchronization calculations are carried out for every dipole (squaring of amplitude, averaging over all trials and time averaging over 16 time points). Both methods were conducted for the study of voluntary hand movement. The results are similar but in contrast to the LAR maps, the LE maps show a better spatial resolution. This is not surprising since the LAR method is limited to the electrode sites whereas with LE the EEG activity is projected onto the source space. Furthermore, the LE method counteracts the deblurring caused by the poorly conducting skull. Linear Estimation depends on several assumptions about the source space, volume conductor and the regularization parameter. Further investigation is needed to evaluate the application of LE for the study of Event-Related EEG phenomena.