Showing papers by "Gert Pfurtscheller published in 1994"
TL;DR: It is speculated, that this ERS represents a short lasting 'idling state' of hand area neurons when other body parts are moved and is circumscribed and found at electrodes overlying both cortical hand areas.
355 citations
TL;DR: Event-related desynchronization is the short-lasting attenuation or blocking of rhythms within the alpha (beta) band and reflects primary visual processing and feature extraction, the latter is more related to cognitive processing and mechanisms of attention.
192 citations
TL;DR: It was found that, based on single EEG trials, the data from the 4 movements could be differentiated with an accuracy of 70% when alpha and gamma band activity were used but only with 58% in the case of the alpha band activity alone.
181 citations
TL;DR: In this article, the same cortical areas are involved in desynchronization of μ and central β rhythms, 56-channel EEG recordings were made during right and left-finger flexions in three normal subjects.
105 citations
TL;DR: Estimation of event-related coherence (ERCoh) and its application to the planning and execution of self-paced index finger movement complements the event- related desynchronization (ERD) measurements of rhythms within the alpha band.
Abstract: This 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.
74 citations
TL;DR: Voluntary finger movements result in a maximal ERD in the 10–12 Hz band close to electrodes C3 and C4, overlying the sensorimotor hand areas, which is not very pronounced with EEG data recorded against a common reference electrode.
Abstract: Voluntary 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.
70 citations
01 Jan 1994
TL;DR: Two feature selection methods, a distinction-sensitive learning vector quantizer (DSLVQ) and a genetic algorithm (GA) approach, are applied to multichannel electroencephalogram (EEG) patterns, showing the importance of methods automatically selecting the most distinctive out of a number of available features.
Abstract: Two feature selection methods, a distinction-sensitive learning vector quantizer (DSLVQ) and a genetic algorithm (GA) approach, are applied to multichannel electroencephalogram (EEG) patterns. It is shown how DSLVQ adjusts the influence of different input features according to their relevance for classification. Using a weighted distance function DSLVQ thereby performs feature selection along with classification. The results are compared with those of a GA which minimizes the number of features taken for classification while maximizing classification performance. The multichannel EEG patterns used in this paper stem from a study for the construction of a brain-computer interface, which is a system designed for handicapped persons to help them use their EEG for control of their environment. For such a system, reliable EEG classification, i.e. differentiation of several distinctive EEG patterns, is vital. In practice the number of electrodes for EEG recordings can be high (up to 56 and more) and different frequency bands and time intervals for each electrode can be used for classification simultaneously. This shows the importance of methods automatically selecting the most distinctive out of a number of available features. >
43 citations
17 Sep 1994
TL;DR: The new setup of the Graz Brain-Computer Interface (BCI) system II, which is based on on-line classification of EEG patterns to determine which of three kinds of movement is planned by a subject, is described.
Abstract: This paper describes the new setup of the Graz Brain-Computer Interface (BCI) system II, which is based on on-line classification of EEG patterns to determine which of three kinds of movement is planned by a subject. This classification can be exploited for on-line control which may constitute a great help for handicapped persons in the future.
40 citations
TL;DR: A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.
Abstract: The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.
39 citations
TL;DR: Classification of non-averaged task-related EEG responses with different types of classifier, including self-organising feature map and learning vector quantiser, K-mean, back-propagation and a combination of the last two, is reported.
Abstract: Classification of non-averaged task-related EEG responses with different types of classifier, including self-organising feature map and learning vector quantiser, K-mean, back-propagation and a combination of the last two, is reported. EEG data are collected from approximately one second periods prior to movement of the right or left index finger. A cue stimulus indicating which hand to use is employed. Feature vectors are formed by concatenating spatial information from different EEG electrodes and temporal information from different time incidents during the planning of hand movement. Power values of the most reactive frequencies within the extended alpha-band (5–16 Hz) are used as features. The features are derived from an autoregressive model fitted to the EEG signals. The performance of the classifiers and their ability to learn and generalise is tested with 200 arbitrarily selected event-related EEG data from a normal subject. Classification accuracies as high as 85–90% are achieved with the methods described here. A comparison of the classifiers is made.
36 citations
TL;DR: It was found by DSLVQ that the most important electrode positions for differentiation between planning of left and right finger movement overlie cortical finger/hand areas over both hemispheres.
Abstract: One major question in designing an EEG-based Brain Computer Interface to bypass the normal motor pathways is the selection of proper electrode positions. This study investigates electrode selection with a Distinction Sensitive Learning Vector Quantizer (DSLVQ). DSLVQ is an extended Learning Vector Quantizer (LVQ) which employs a weighted distance function for dynamical scaling and feature selection. The data analysed and classified were 56-channel EEG recordings over sensorimotor areas during preparation for discrete left or right index finger flexions. Data from 3 subjects are reported. It was found by DSLVQ that the most important electrode positions for differentiation between planning of left and right finger movement overlie cortical finger/hand areas over both hemispheres.
27 Jun 1994
TL;DR: A Distinction Sensitive Learning Vector Quantizer (DSLVQ), based on the LVQ3 algorithm, is introduced which automatically adjusts the influence of the input features according to their observed relevance for classification.
Abstract: A Distinction Sensitive Learning Vector Quantizer (DSLVQ), based on the LVQ3 algorithm, is introduced which automatically adjusts the influence of the input features according to their observed relevance for classification. DSLVQ is less sensitive to noisy features than standard LVQ and its importance adjustments are transparent and can be exploited for input data feature selection. As an example, the algorithm is applied to the classification of two artificial data sets: Breiman's (1984) waveform data and Kohonen's "hard" classification task. >
01 Jan 1994
TL;DR: The brain has the ability to generate rhythmic activities in a broad range, whereby components within the alpha, beta and gamma bands play an important role in sensory processing and motor behavior.
Abstract: The brain has the ability to generate rhythmic activities in a broad range, whereby components within the alpha, beta and gamma bands play an important role in sensory processing and motor behavior. One of the first groups to record EEG from the intact skull during sensory stimulation was Jasper and Andrew (1938), who reported on alpha components from 8–13 Hz, beta components from 17–30 Hz and possible gamma components from 35–48 Hz. Their main finding was that precentral beta potentials were independent of the occipital alpha potentials in response to sensory stimulation.
TL;DR: Computer-assisted analysis in the time and frequency domain demonstrated that during quiet sleep (stage 3/4), the cardiorespiratory coupling was significantly increased as compared to active sleep (REM sleep).
Abstract: Heart rate variability and thoracal respiratory movements were examined in two babies aged six months during eight hour polysomnographic recordings. Computer-assisted analysis in the time and frequency domain were performed to investigate the mechanisms underlying cardiorespiratory control. Coherence and phase relation between cardiac cycles and respiration were investigated in detail. Both methods demonstrated that during quiet sleep (stage 3/4), the cardiorespiratory coupling was significantly increased as compared to active sleep (REM sleep).
TL;DR: The data provided by multiparametric neurovegetative monitoring support the evaluation of complex regulatory mechanisms of respiratory and cardiovascular function and their adaptability after orthotopic heart transplantation.
Abstract: The effects of orthotopic heart transplantation on spontaneous fluctuations of the respiration rate and heart rate were studied with a computer-assisted system for neurovegetative monitoring in 22 patients (mean age +/- SD: 48.7 +/- 9.4 years) 19.5 +/- 14.4 months after transplantation. The control group consisted of 12 healthy volunteers (mean age +/- SD: 38.7 +/- 6.6 years). The mean (+/- SE) respiratory rate was higher in the transplantation group than in the control group (17.7 +/- 0.8/min vs. 14.6 +/- 1.1 breaths/min, P < 0.2). The mean variability of the respiratory rate was smaller in the transplant patients than in the controls (3.7 +/- 0.3 vs. 2.8 +/- 0.4, P < 0.2). The heart rate variability coefficient in the patients after transplantation was lower than that in the controls (1.3 +/- 0.1% vs. 6.9 +/- 0.5%, P < 0.001). Spectral analysis of heart rate variability showed a smaller decrease of variability of respiration (P < 0.05) than of blood pressure regulation (P < 0.001) or of the angiotensin-renin system (P < 0.001). A separate group of 7 transplant patients (mean age 51.0 +/- 7.7 years) had activated cardiac pacemakers and thus no spontaneous physiologic heart rate oscillations. The variability of the respiratory rate in these patients was lower than in the other 22 transplant patients (1.8 +/- 0.2 vs. 3.7 +/- 0.3, P < 0.001). The data provided by multiparametric neurovegetative monitoring support the evaluation of complex regulatory mechanisms of respiratory and cardiovascular function and their adaptability after orthotopic heart transplantation.
01 Jan 1994
TL;DR: Bei komatosen Patienten nach schwerem Schadel-Hirn-Trauma (SHT) oder hypoxischer Hirnschadigung sowie zerebrovaskularen Katastrophen kommt es in einer erheblichen Anzahl zu einem letalen Verlauf.
Abstract: Bei komatosen Patienten nach schwerem Schadel-Hirn-Trauma (SHT) oder hypoxischer Hirnschadigung sowie zerebrovaskularen Katastrophen kommt es in einer erheblichen Anzahl zu einem letalen Verlauf. Daruber hinaus bleibt ein betrachtlicher Anteil unter den Uberlebenden infolge neurologischer Defizite schwer behindert oder pflegebedurftig. Es erklart sich somit das Interesse, das genauere Ausmas einer Lasion in bezug auf die erwartende Einschrankung der neuronalen Funktion zu erfassen.
01 Jan 1994
TL;DR: Evoked potentials (EP) can be used to study brain functions in the intensive care unit and the operating room, in addition to spontaneous cerebral electrical activity (electroencephalography, EEG).
Abstract: The activation of neuronal structures and systems in the brain is accompanied by changes of the electrical potentials recorded from the scalp. Evoked potentials (EP) can be used to study brain functions in the intensive care unit and the operating room, in addition to spontaneous cerebral electrical activity (electroencephalography, EEG) [1-5].
03 Nov 1994
TL;DR: A PC-based monitoring system has been developed, able to acquire and analyse up to 32 physiological signals of different modality with maximum overall sample frequency of 48 kHz, which was used to study noninvasively stroke volume, cardiac output, blood pressure, total peripheral resistance and left ventricular ejection time online.
Abstract: A PC-based monitoring system has been developed, which is able to acquire and analyse up to 32 physiological signals of different modality with maximum overall sample frequency of 48 kHz. New algorithms can be added and polygraphical presentation of results can be adjusted for special applications. The system was used to study noninvasively stroke volume, cardiac output, blood pressure, total peripheral resistance and left ventricular ejection time online. >