nils-herrmann/clean_scirepeval huggingface dataset:
Detection of movement-related patterns in ongoing single-channel electrocorticogram

Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization 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.
Frequency Component Selection for an ECoG-based Brain-Computer Interface. Auswahl von Frequenzkomponenten aus ECoG-Signalen zur Steuerung eines Brain Computer Interface

The aim of the present study was to investigate the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI). For this purpose the time-frequency ERD/ERS map and the distinction sensitive learning vector quantization (DSLVQ) are applied to ECoG from three subjects, recorded during a self-paced finger movement. The results show that the ERD/ERS pattern found in ECoG generally matches the ERD/ERS pattern found in EEG recordings, but has an increased prevalence of frequency components in the beta range.
Movement and ERD/ERS

One characteristic feature of the brain is its ability to generate rhythmic potentials or oscillatory activity. Already in 1949 Jasper and Penfield discovered this fact and discussed the relationship between alpha and beta rhythms and their functioning in relation to underlying neural networks. The frequency of brain oscillations depends both on membrane properties of single neurons and the organization and interconnectivity of networks to which they belong (Lopes da Silva, 1991). Such a network can either comprise a large number of neurons controlled, for example, by thalamo-cortical feedback loops or only a small number of neurons interconnected, for example, by intra-cortical feedback loops. Coherent activity in large neuronal pools can result in high amplitude and low frequency oscillations (e. g. alpha band rhythms), whereas synchrony in localized neuronal pools can be the source of gamma oscillations (Lopes da Silva and Pfortscheller, 1999). The dynamic of such a network can result in phasic changes in the synchrony of cell populations due to externally or internally paced events and lead to characteristic EEG patterns. Two such pattern types are observed, the event-related desynchronization, or ERD, in form of an amplitude attenuation and the event-related synchronization, or ERS, in form of an enhancement of specific frequency components (Pfortscheller and Lopes da Silva, 1999a, Pfortscheller and Lopes da Silva, 1999b).
Implementation of a telemonitoring system for the control of an EEG-based brain-computer interface

By the use of a brain-computer interface (BCI), it is possible for completely paralyzed patients, who have lost their ability to speak, to have a new possibility to communicate with their environment. The training with such a BCI system can be performed at the patient's home, if there is a responsible person present who is familiar with the system. This person has to adjust different parameters and to adapt the training individually to each patient. Since this function is usually taken over by the developers of the system, the number of patients who can be included in regular BCI training is restricted due to geographical distances. This paper describes the implementation of a telemonitoring system, which makes it possible for the developer to control and supervise the BCI training from his or her own place of work. First experiences with a patient living far away from the developer's lab are reported.
Presence Research and EEG

The fields of presence research and electroencephalography (EEG) are related in, at least, two ways. Firstly, EEG can be used to analyse the neurophysiological phenomena related to presence research. For example, EEG might be useful to investigate the question of ‘breaks in presence’. Secondly, EEG can be used to control external devices with a so-called “brain computer interface” (BCI). Such a BCI might be also used for controlling a virtual environment.
Estimating the Mutual Information of an EEG-based Brain-Computer Interface

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.
[Mental activity hand orthosis control using the EEG: a case study].

A report is given on the realization of a steering mechanism of a hand orthosis for a patient with paraplegia. An EEG-based Brain-Computer Interface (BCI) was used here for the first time, transferring purely mental activity to a control signal. This means that the patient has the capability to open or close the hand orthosis only by imagination of a movement. At this time, after a training period of about four months, the patient is able to move the hand orthosis with a certainty of almost hundred percent. The restored grasp function was verified by a grasp function test. Results are compared to those obtained using a conventional EMG-controlled orthosis.
Information transfer rate in a five-classes brain-computer interface

The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain-computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the EEG patterns is based on band power estimates and hidden Markov models. We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each subject, different combinations of three tasks resulted in the best performance.
Rapid prototyping of an EEG-based brain-computer interface (BCI)

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional DSP board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive model and linear discriminant analysis.
EOG AND ECG MINIMIZATION BASED ON REGRESSION ANALYSIS

A method based on regression analysis is presented that can be used for automatic minimization of EOG and ECG artifacts in the sleep EEG.  It is known that the non-cortical activity gives a contribution to the EEG recordings. Electrooculogram (EOG), the electrocardiagram (ECG) and muscle activity (EMG) are the most important non-cortical sources. For sleep analysis it is important to know the undisturbed EEG. Therefore, it is necessary to detected and/or reduce non-cortical sources on EEG. Additionally, the detection/removal methods must be fully automatic. In the SIESTA project EOG, ECG as well as EEG are recorded. Therefore, it is self-evident to use these signals to reduce the influence of the EOG and the ECG. The EMG channel is not used, because the activity of different muscles is concerned. Therefore EOG and ECG signals are used for regression analysis, with the aim to reduce, at least, the linear and time-invariant part of the superposition. Elbert et al. (1985) showed theoretically how the EOG influences the scalp potentials. Berg and Scherg (1994) assumed that the EOG source is a dipole. They applied a multiple source eye correction (MSEC) method. Based on that approach Ille et al. (1997) calculated a Spatial Filter matrix with Principle Component Analysis (PCA). Lagerlund et al. (1997) applied PCA for identifying different kinds of artifacts. The artifact components were identified by visual inspection of the principle components.
Distinction of different fingers based on induced beta frequencies

< >M i l l . i t n i v a c i i v i l v in the ramie of Ι4.Ϊ5 l l / is a i h a i . i f t c M s i k leahi ie ol the Kolandic area and in close l u n u i i M i . i l ιc l . i t ion to mulei lying cortical networks. \ lo \emcni or exen motor imagery can block or dv-sMiclirom/e central beta oscillations and results in a cnc t i inse i ihed beta Ι . Κ Γ ) close to the corresponding representation areas [ 1 3 ) . Al ter termination ol" the motor mental action the desynchronized beta oscillations d i N p l a v a last recovery or even a rebound above the icsimg lex el. This beta rebound or beta event-related svnchron i /a t ion l l . R S ) has the maximum about 700 ms a l t e r movement-offset and displays a somatotopic orgam/ation [13|. It xvas shown recently in a neuromagnetic study, that also electrical median nerve s t imula t ion results in a beta ERS localized over the hand representation area . A similar beta ERS after median nerve stimulation was reported in the EEG as well [5]. I he goal ol this study was to investigate the frequency of induced beta oscillations after stimulation of each fingertip of one hand and to obtain an answer to the question whether the individual fingers can be differentiated by the frequency of the induced postst imulus beta oscillations. For this purpose we analyzed the induced beta oscillations in single trials by using the Matching Pursuit (MP) algorithm .
Asymmetric hemisphere modeling in an offline brain-computer interface

Classification of the electroencephalogram (EEG) during motor imagery of the left or right hand can be performed using a classifier comprising two hidden Markov models (HMMs) describing the spatio-temporal patterns related to the imagination. Due to the known asymmetries during motor imagery of rightand left-hand movement, an HMM-based classifier allowing asymmetrical structures is introduced. The comparison between such a system and a symmetrical one is based on the error rate of classification. The results for EEG data collected during 20 sessions from five subjects demonstrate a significant improvement of 9% for the classification accuracy for the asymmetric classifiers. The selection of the DAM for classification is done using a variant of genetic algorithms (GAs); namely, the adaptive reservoir genetic algorithm (ARGA).
Mechanical Stimulation of the Fingertip Can Induce Bursts of &bgr; Oscillations in Sensorimotor Areas

Short mechanical stimulation of the fingertip was studied in 10 subjects using 34 closely spaced EEG electrodes overlying sensorimotor areas. The tactile skin stimulation device consisted of a dot matrix printer head. Each stimulus lasted 10 msec with an interstimulus interval of 4 seconds. Tactile stimulation induced &bgr; oscillations, which were largest in the 16 to 20-Hz band, with a maximum centered within 1 second after stimulation. These &bgr; oscillations were found over both hemispheres with a clear dominance over the contralateral hand representation area.
Automatic differentiation of multichannel EEG signals

Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. The authors present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.
Adaptive autoregressive parameters used in BCI research

An EEG-based BCI system is composed of different components including, an EEG recording, feature extraction and/or classification and online feedback. The system in Tübingen uses slow EEG potentials to control the feedback. In Albany, spectral parameters of the EEG are cumulated and used for on-line feedback. Since the beginning the Graz BCI system has been based on spectral analysis of the EEG in combination with some classifier (see Fig. 1). It is obvious that the subject’s ability to learn controlling his/her EEG patterns, does depend on the reliability of the feedback. One of the key components in a BCI system is the extraction of EEG features, best suitable to monitor the dynamics of different brain states.
„Resonance-like“ Frequencies of Sensorimotor Areas Evoked by Repetitive Tactile Stimulation - Resonanzeffekte in sensomotorischen Arealen, evoziert durch rhythmische taktile Stimulation

The aim of the present study was to investigate resonance-like frequencies of somatosensory steady-state evoked potentials (SSSEPs) during mechanical stimulation of the left and right index finger. The resonance effects were studied in a group of normal subjects at 8 frequencies between 17 Hz and 31 Hz. Band power was computed in 2 s epochs in eight 2 Hz beta bands starting from 16-18 Hz up to 30-32 Hz and referred to a baseline prior to stimulation. For each subject the band with the largest band power increase was determined on electrodes overlying the contralateral hand area. The band power maxima and therewith the "resonance-like" frequencies were found around 27 Hz.
Prosthetic Control by an EEG-based Brain-Computer Interface ( BCI )

The real-time analyses of oscillatory EEG components during right and left hand movement imagination allows the control of an electric device. Such a system, called brain-computer interface (BCI), can be used e.g. by patients who are totally paralyzed (e.g. Amyotrophic Lateral Sclerosis) to communicate with their environment. The paper demonstrates a system that utilizes the EEG for the control of a hand prosthesis.
Source Localization of Induced Cortical Oscillations During Tactile Finger Stimulation - Quellokalisation von induzierten Gehirnoszillationen bei taktiler Fingerstimulation

We investigated the EEG beta event-related synchronization (ERS) after tactile finger stimulation in three subjects. Prior studies from our group using electrical stimulation and self-paced movement showed a beta rebound within one second after stimulation respectively movement offset. As the tactile-stimulation-data showed a similar ERS behaviour, we extracted the cortical sources for this beta rebound by the linear estimation method in order to see whether the representation areas of different fingers were distinguishable (as is possible with MEG data). Although realistic head models of two subjects were used for the calculations the fingers could not be spatially distinguished. However, regarding the whole spatio-temporal pattern of the ERS for different fingers clear differences can be observed.
EEG pattern recognition through multi-stream evidence combination

EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently developed to address a related problem of recogniser robustness to uncontrollable signal variation which also occurs in automatic speech recognition (ASR). In this article we consider how some of the proved advantages of the "multi-stream combination" and "tandem" approaches in HMM/ANN hybrid based ASR can possibly be applied to improve the performance of EEG recognition.
Motor imagery and direct brain-computer communication

Motor imagery can modify the neuronal activity in the primary sensorimotor areas in a very similar way as observable with a real executed movement. One part of EEG-based brain-computer interfaces (BCI) is based on the recording and classification of circumscribed and transient EEG changes during different types of motor imagery such as, e.g., imagination of left-hand, right-hand, or foot movement. Features such as, e.g., band power or adaptive autoregressive parameters are either extracted in bipolar EEG recordings overlaying sensorimotor areas or from an array of electrodes located over central and neighboring areas. For the classification of the features, linear discrimination analysis and neural networks are used. Characteristic for the Graz BCI is that a classifier is set up in a learning session and updated after one or more sessions with online feedback using the procedure of "rapid prototyping." As a result, a discrimination of two brain states (e.g., leftversus right-hand movement imagination) can be reached within only a few days of training. At this time, a tetraplegic patient is able to operate an EEG-based control of a hand orthosis with nearly 100% classification accuracy by mental imagination of specific motor commands.
Describing the sleep EEG with an Adaptive Autoregressive Model

Sleep analysis using AAR parameters (healthy subject). On top, the hypnogram scored by an expert is shown. Below, the classification with AAR parameters using channels C3-M2 and C4-M1 is given for deep sleep (green line), REM (red line) and awake (blue line) – the highest curve indicates the appropriate sleep stage. The bottom plot shows the variance of the inverse filtered process (logarithmic scale). Spikes indicate artefacts and transient events. EEG processing The EEG of polygraphic recordings (SIESTA database)3 from eight European sleep labs was analysed; ECG artefacts and line interference were minimised with regression analysis and 50Hz notch filtering. The data were downsampled to 100Hz. Next, the AAR parameters were estimated with Kalman filtering assuming a multivariate random walk of AAR parameters2,4.
Describing the sleep EEG with an Adaptive

The spectral information of the sleep EEG is an important indicator for the sleep stage1. Adaptive autoregressive parameters can describe the time-varying spectrum. The AR spectrum is a maximum entropy spectral estimator, which is optimal with respect to the number of parameters. No selection of frequency bands is required if the AR parameters are combined by a classifier. Adaptive estimation algorithms are useful for the on-line and real- time sleep analysis2.
Current trends in Graz Brain-Computer Interface (BCI) research.

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.
Optimal spatial filtering of single trial EEG during imagined hand movement.

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.
Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI).

Electroencephalogram (EEG) recordings during right and left motor imagery allow one to establish a new communication channel for, e.g., patients with amyotrophic lateral sclerosis. Such an EEG-based brain-computer interface (BCI) can be used to develop a simple binary response for the control of a device. Three subjects participated in a series of on-line sessions to test if it is possible to use common spatial patterns to analyze EEG in real time in order to give feedback to the subjects. Furthermore, the classification accuracy that can be achieved after only three days of training was investigated. The patterns are estimated from a set of multichannel EEG data by the method of common spatial patterns and reflect the specific activation of cortical areas. By construction, common spatial patterns weight each electrode according to its importance to the discrimination task and suppress noise in individual channels by using correlations between neighboring electrodes. Experiments with three subjects resulted in an error rate of 2, 6 and 14% during on-line discrimination of left- and right-hand motor imagery after three days of training and make common spatial patterns a promising method for an EEG-based brain-computer interface.
Spindle Filter Optimisation with Receiver Operating Characteristics-Curves

The automatic detection of sleep spindles can be accomplished with filter methods. The optimisation of filter characteristics is necessary to improve the performance of the detection. Raw EEG data were filtered with varying basebands in the frequency range of sleep spindles. The outputs were taken to calculate Receiver Operating Characteristics-Curves (ROC-Curves). The area under the ROC-curve (AUC) served as the only optimisation parameter. The maximum AUC had 0.962 square units. This result corresponded to a filter-function with edge-frequencies of 12Hz and 16.8Hz. The shown optimisation procedure is not limited to any number of free parameters. Solely data marked by an expert is needed. The ROCcurves can be further used to find a suitable detection-threshold for sleep spindles.