nils-herrmann/clean_scirepeval huggingface dataset:
Continuous multivariable monitoring in neurological intensive care patients-preliminary reports on four cases

Evoked potential monitoring is a standard examination method in neurological intensive therapy units. Previously, multimodality observation was only possible in follow-up examinations. First experience with a new bed-side system continuously monitoring 12 neurophysiological and clinical parameters is reported. It consists of a personal computer and various stimulation units. EEG activity, median nerve somatosensory evoked potentials (SEPs) and brainstem auditory evoked potentials (BAEPs) are recorded. Additionally, EEG spectral band power, heart rate, heart rate variability, intracranial presure, body temperature, expiratory PCO2, blood pressure and transcutaneously measured oxygenation can be monitored. This paper reports on 4 exemplary cases of the 33 patients we have monitored to date, illustrating the principles and main advantages of the system. The system was developed to support the observation of ICU patients as well as to aid therapeutic decisions. It supports the clinical determination of brain death by specifying the deterioration of various neurological systems.
SHORT COMMUNICATIONS: Semantic memory retrieval: cortical couplings in object recognition in the N400 window

To characterize the regional changes in neuronal couplings and information transfer related to semantic aspects of object recognition in humans we used partial‐directed EEG‐coherence analysis (PDC). We examined the differences of processing recognizable and unrecognizable pictures as reflected by changes in cortical networks within the time‐window of a determined event‐related potential (ERP) component, namely the N400. Fourteen participants performed an image recognition task, while sequentially confronted with pictures of recognizable and unrecognizable objects. The time‐window of N400 as indicative of object semantics was defined from the ERP. Differences of PDC in the beta‐band between these tasks were represented topographically as patterns of electrical couplings, possibly indicating changing degrees of functional cooperation between brain areas. Successful memory retrieval of picture meaning appears to be supported by networks comprising left temporal and parietal regions and bilateral frontal brain areas.
An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate

An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents an asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate /spl sigma/, the number of correctly spelled letters/min, up to /spl sigma/=3.38 (average /spl sigma/=1.99).
Heart-Rate Variability and Event-Related ECG in Virtual Environments

Beside standard questionnaires physiological measures can be used to describe the state of Presence in a virtual environment. A total of 21 participants explored a virtual bar in a CAVE like system. The experiment was divided into a baseline-, training- and experimental phase. During the experimental phase breaks in presence (BIPS) in form of whiteouts of the virtual environment scenario were induced. The heart rate variability and the event-related ECG were calculated from the acquired ECG data. The study showed that the heart rate variability can be used as parameter that reflects the physiological state of the participant. The event-related ECG showed the effect of the induced BIPS and of speaking avatars on the subjects.
Restless legs syndrome: changes of induced electroencephalographic beta oscillations-an ERD/ERS study.

Primary or idiopathic restless legs syndrome (RLS) is a sensorimotor disorder of unknown neurophysiologic origin.



Ten patients with RLS and 10 healthy control subjects were investigated. Postmovement beta oscillations (event-related synchronization, ERS) induced by movement of the right index finger were measured by electroencephalography and analyzed.



We found differences between patients and controls for ERS values at electrode positions C3 and Cz. At C3, the lower beta band ERS (14-20 Hz) in the RLS group was 101.2% compared with 27.5% in the control group (P < .05); in the upper beta band, (20-32 Hz) the findings were 97.8% and 29.0%, respectively, for the RLS and control groups (P < .01). At electrode Cz, no significant difference could be found in the lower beta band, but, for the upper beta band, patients showed significantly higher values than did the healthy control subjects (68.5% vs 25.6%, P < .05).



We interpret these findings as a higher need for motor-cortical inhibition in RLS patients due to an increased level of excitation by motor-cortex activation and input from neighboring functionally interrelated cortical areas (hand and foot region). These results reveal new potentially important findings of the neurophysiologic and pathophysiologic origin of primary RLS.
Post-Movement EEG Synchronization Studied with Different High Resolution Methods

In this paper we present a study of spline surface Laplacian (LP), linear estimation (LE) and analytical deblurring (AD) utilized to improve the spatial resolution of single trial EEG data. AD is a method to reconstruct the potential distribution on the cortical surface. The dependency of AD on the electrode grid size as well as the sensitivity to uncorrelated noise and errors in the volume conductor model are investigated in detail and compared with LP. Finally, all methods (LP, LE and AD) are applied to single trial EEG data recorded in three subjects during voluntary and self-paced extension and flexion movements of the right index finger. In each subject postmovement beta oscillations were found in specific frequency bands. Cortical dipolar source strengths were reconstructed by LE and cortex potentials were estimated with AD. Both results are compared with LP calculated from the scalp EEG. All methods, although having different theoretical basis, yield similar results and reveal a maximal event-related synchronization over the left sensorimotor area approximately 500-875 ms after termination of the movement.
Navigating Virtual Reality by Thought: First Steps

We have set up a brain-computer interface to be used as an input device to a highly immersive virtual reality Cave-like system. We have carried out two navigation experiments: three subjects were required to rotate in a virtual bar room by imagining left or right hand movement, and to walk along a single dimension in a virtual street by imagining foot or hand movement. In this paper we focus on the subjective user experience of navigating virtual reality “by thought”, and on the interrelations between BCI and presence.
The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials

Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.
Continuous EEG classification during motor imagery-simulation of an asynchronous BCI

Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.
Lexical memory search during N400: cortical couplings in auditory comprehension

In this study, we applied partial-directed EEG-coherence analysis to assess regional changes in neuronal couplings and information transfer related to semantic processing. We tested the hypothesis whether (and which) processing differences between spoken words and pseudowords are reflected by changes in cortical networks within the time window of a specific event related potential (ERP) component, the N400. Fourteen native speaking German subjects performed a lexical decision paradigm, while being confronted sequentially with two-syllabic nouns and phonologically legal pseudowords. Using ERP analysis, we defined the time window of the N400 effect, known to reflect semantic processing, and, subsequently, we examined the coupling differences. Lexico-semantic memory search appears to be subserved by a network between temporal, parietal and frontal areas, particularly restricted to the left hemisphere.
Funktionelle Elektrostimulation anstatt Operation?

ZusammenfassungZiel dieser Arbeit war es, bei einem Patienten mit kompletter C5-Tetraplegie die Greiffunktion mittels funktioneller Elektrostimulation (FES) wiederherzustellen. Mit 3 Paar Oberflächenelektroden und einer orthetischen Handgelenkstabilisierung konnte ein einfacher Spitzgriff realisiert werden.Die FES wurde entweder mit einem Schalter am Rollstuhl oder erstmalig mit einem EEG-basierten Brain-Computer-Interface (BCI) gesteuert. Mit dem Einsatz dieses Stimulationsystems war es unserem Patienten zum 1. Mal nach dem Unfall möglich, selbständig und ohne fremde Hilfe aus einem Glas zu trinken.AbstractThe 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.
Application of the correlation integral to respiratory data of infants during REM sleep

Non-linear time sequence analysis has been performed on infant sleep measurement data in order to obtain more information about the respiratory processes. As a first step, respiration data during REM sleep were analysed with methods from non-linear dynamics, especially, the correlation integral and the slope of its log-log plot, representing the correlation dimension. Before calculation of the correlation integral, a special kind of filtering has to be applied to the data. This filtering algorithm is a state space and singular value decomposition-based noise reduction method, and it is used to separate the noise and signal subspaces. The dynamics of a signal (in our case data from the respiratory process) and its degrees of freedom can be characterised by the correlation integral and by the correlation dimension, respectively. The main result of this study is that the highly irregular-looking breathing patterns during REM sleep could be described by a deterministic system, and finally the physiological significance of this finding is discussed.
Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis

Highly accurate asynchronous detection of movement related patterns in individual electrocorticogram channels has been shown using detection based on either event-related potentials (ERPs) or event-related desynchronization and synchronization (ERD/ERS). A method using wavelet-packet features selected with a genetic algorithm was proposed to simultaneously detect ERP and ERD/ERS and was tested on data from seven subjects and four motor tasks. The proposed wavelet method performed better than previous methods with perfect detection for four subject/task combinations and hit percentages greater than 90% with false positive percentages less than 15% for at least one task for all seven subjects.
Navigation in Virutal Environments through Motor Imagery

In this paper we report on the navigation in a virtual environment by the output signal of an EEG-based Brain-Computer Interface (BCI). Such a BCI transforms bioelectrical brain signals, modified by mental activity into a control signal. At this time only 1-D or 2-D BCI feedbacks are used. The graphical possibilities of virtual reality (VR) should help to improve BCI-feedback presentations and create new paradigms, with the intention to obtain a better control of the BCI. In this study the subjects had to imagine left or right hand movements and thereby exploring a virtual conference room. With a left hand motor imagery the subject turned in the room to the left and vice versa. Three trained subjects reached 77% to 100% classification accuracy in the course of the experimental sessions.
Conversion of EEG activity into cursor movement by a brain-computer interface (BCI)

The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.
Feature Mapping using PCA , Locally Linear Embedding and Isometric Feature Mapping for EEG-based Brain Computer Interface

To enable the user to visualize their own brain activity is an essential part of a Brain-Computer Interface (BCI). For visualization, we need a dimension reduction algorithm, because only a few relevant components of the high-dimensional feature vectors, extracted from the electroencephalogram (EEG) signals, can be visualized. For this purpose, three feature mapping methods, Principal Component Analysis (PCA), Locally Linear Embedding (LLE) and Isometric Feature Mapping (Isomap) were investigated and compared to each other.
Walking through a virtual city by thought

This paper gives a short overview of the feasibility of walking through a virtual city by using motor imagery. Therefore an electroencephalogram-based brain-computer interface (BCI) is combined with virtual reality technology. A BCI transforms bioelectrical brain signals, modulated by mental activity (e.g. imagination of foot or right hand movements), into a control signal. This signal is used to walk forward / backward or to remain stationary inside a virtual city. Results of the first experimental sessions are presented.
"Virtual keyboard" controlled by spontaneous EEG activity

A "virtual keyboard" (VK) is a letter spelling device operated for example by spontaneous electroencephalogram (EEG), whereby the EEC is modulated by mental hand and leg motor imagery. We report on three able-bodied subjects, operating the VK. The ability in the use of the VK varies between 0.85 and 0.5 letters/min in error-free writing.
Detection of ERP and ERD/ERS patterns in single ECoG channels

Wavelet packet analysis and a genetic algorithm were used to detect movement-related patterns in single channel electrocorticogram (ECoG). Detection accuracies of more than 90% hits and less than 10% false positives were found. The findings show that the detection of movement-related patterns in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.
Graz-BCI: state of the art and clinical applications

The Graz-brain-computer interface (BCI) is a cue-based system using the imagery of motor action as the appropriate mental task. Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported. Additionally, it is demonstrated how information transfer rates of 17 b/min can be acquired by real time classification of oscillatory activity.
Electrocorticogram as the basis for a direct brain interface: Opportunities for improved detection accuracy

A direct brain interface (DBI) based on the detection of event-related potentials (ERPs) in human electrocorticogram (ECoG) is under development. Accurate detection has been demonstrated with this approach (near 100% on a few channels) using a single-channel cross-correlation template matching (CCTM) method. Several opportunities for improved detection accuracy have been identified. Detection using a multiple-channel CCTM method and a variety of detection methods that take advantage of the simultaneous occurrence of ERPs and event-related desynchronization/synchronization (ERD/ERS) have been demonstrated to offer potential for improved detection accuracy.
Critical Decision-Speed and Information Transfer in the “Graz Brain–Computer Interface”

The “Graz Brain–Computer Interface (BCI)” transforms changes in oscillatory EEG activity into control signals for external devices and feedback. These changes are induced by various motor imageries performed by the user. For this study, 2 different types of motor imagery (movement of the right vs. left hand or both feet) were classified by processing 2 bipolar EEG-channels (derived at electrode positions C3 and C4). After a few sessions, within some weeks, 4 young paraplegic patients learned to control the BCI. In accordance with the participants, decision-speed (trial length) was varied and the information transfer rate (ITR) was calculated for each run. All experimental runs have been feedback-runs employing a simple computer-game-like paradigm. A falling ball had to be led into a randomly marked target halfway down the screen. The horizontal position was controlled by the BCI-output signal and the trial length was varied by the investigator across runs. The goal was to find values for trial length enabling a maximum ITR. Three out of 4 participants had good results after a few runs. Analysis of their last 2 experimental sessions, each containing between 10 and 16 runs, showed that the trial length can be reduced to values around 2 s to obtain the highest possible information transfer. Attainable ITRs were between 5 and 17 bit/min depending on the participant's performance and condition.
[EEG-based communication--a new concept for rehabilitative support in patients with severe motor impairment].

This paper describes a paralyzed patient diagnosed with severe infantile cerebral palsy, trained over a period of several months to use an EEG-based brain-computer interface (BCI) for verbal communication. The patient learned to "produce" two distinct EEG patterns by mental imagery and to use this skill for BCI-controlled spelling. The EEG feedback training was conducted at a clinic for Assisted Communications, supervised from a distant laboratory with the help of a telemonitoring system. As a function of training sessions significant learning progress was found, resulting in an average accuracy level of 70% correct responses for letter selection. At present, "copy spelling" can be performed with a rate of approximately one letter per minute. The proposed communication device, the "Virtual Keyboard", may improve actual levels of communication ability in completely paralyzed patients. "Telemonitoring-assisted" training facilitates clinical application in a larger number of patients.
Induced Oscillations in the Alpha Band: Functional Meaning

The phenomena of event‐related desynchronization (ERD) and synchronization (ERS) reflect the dynamics of neural networks and can be observed on different scalp locations at the same moment of time. Whereas on one cortical area a focal 10‐Hz ERD can be found, other areas can display a 10‐Hz ERS. This phenomenon is called focal ERD/surround ERS and is interpreted as a correlate of an activated cortical area (ERD) and simultaneously deactivated or inhibited other areas. The induced oscillations (ERS) are dominant in the 10‐ to 13‐Hz band and very likely mediated by thalamic gating.
How many people are able to operate an EEG-based brain-computer interface (BCI)?

Ninety-nine healthy people participated in a brain-computer interface (BCI) field study conducted at an exposition held in Graz, Austria. Each subject spent 20-30 min on a two-session BCI investigation. The first session consisted of 40 trials conducted without feedback. Then, a subject-specific classifier was set up to provide the subject with feedback, and the second session - 40 trials in which the subject had to control a horizontal bar on a computer screen - was conducted. Subjects were instructed to imagine a right-hand movement or a foot movement after a cue stimulus depending on the direction of an arrow. Bipolar electrodes were mounted over the right-hand representation area and over the foot representation area. Classification results achieved with 1) an adaptive autoregressive model (39 subjects) and 2) band power estimation (60 subjects) are presented. Roughly 93% of the subjects were able to achieve classification accuracy above 60% after two sessions of training.