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Showing papers by "Gert Pfurtscheller published in 2005"


Journal ArticleDOI
TL;DR: To improve motor-imagery-based BCI control, user training should emphasize kinesthetic experiences instead of visual representations of actions.

658 citations


Journal ArticleDOI
TL;DR: This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations and revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics.
Abstract: 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%.

496 citations


Journal ArticleDOI
TL;DR: Evidence is given that Brain-Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions and the fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes.

465 citations


Journal ArticleDOI
TL;DR: The results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst, while the single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject.
Abstract: 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.

386 citations


Journal ArticleDOI
TL;DR: The midcentrally located beta rebound is interpreted as an electrophysiological correlate of a simultaneous "resetting" of overlapping neural networks in the foot representation areas and the SMA.

265 citations



Journal ArticleDOI
TL;DR: By the application of FES, noninvasive restoration of hand grasp function in a tetraplegic patient was achieved and the patient was able to grasp a glass with the paralyzed hand completely on his own without additional help or other technical aids.
Abstract: 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.

119 citations


Journal ArticleDOI
TL;DR: The hypothesis was put forward that these kinds of changes in flow of electrical brain activity are connected with the specific information processing.
Abstract: Objectives: The objective of the paper was the determination of electrical brain activity propagation in sensorimotor areas during hand movement imagery. Methods: 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. Results: 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. Conclusions: The hypothesis was put forward that these kinds of changes in flow of electrical brain activity are connected with the specific information processing.

53 citations


01 Jan 2005
TL;DR: 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.
Abstract: 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.

33 citations


Journal ArticleDOI
TL;DR: It was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.
Abstract: We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naive subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.

31 citations



Journal ArticleDOI
TL;DR: This paper investigates whether additional information can be found when calculating the amount of synchronization between two electrode channels by using a phase locking measurement called the phase locking value (PLV).

Journal ArticleDOI
TL;DR: In this paper, the authors describe the possibility of navigating in a virtual environment using the output signal of an EEG-based Brain-Computer Interface (BCI) using the graphical capabilities of virtual reality (VR) and improve feedback presentation.
Abstract: In this paper, we describe the possibility of navigating in a virtual environment using the output signal of an EEG-based Brain-Computer Interface (BCI). The graphical capabilities of virtual reality (VR) should help to create new BCI-paradigms and improve feedback presentation. The objective of this combination is to enhance the subject's learning process of gaining control of the BCI. In this study, the participant had to imagine left or right hand movements while exploring a virtual conference room. By imaging a left hand movement the subject turned virtually to the left inside the room and with right hand imagery to the right. In fact, three trained subjects reached 80% to 100% BCI classification accuracy in the course of the experimental sessions. All subjects were able to achieve a rotation in the VR to the left or right by approximately 45 degrees during one trial.

Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

Journal ArticleDOI
TL;DR: The correlation analysis indicated a close relationship between the EEG activity and the heart rate and heart rate variability, and it was shown for the first time that the beta ERS in the 14-18 Hz frequency range (post-movement beta E RS) was significantly reduced at high altitude.

Journal ArticleDOI
TL;DR: It was found that electroencephalographic bursts of slow waves during TA are coupled with an acceleration of the HR in a group of preterm infants with a mean conceptional age (CA) of 36 weeks.
Abstract: Continuous and simultaneous registration of electroencephalogram (EEG) and heart rate (HR) pattern in preterm infants can give information about the functioning of central nervous system and the integrity of the autonomic nervous system. The developmental and behavioural state determine the pattern of EEG activity. A discontinuous EEG activity also known as ‘Trace alternant’ (TA) in preterm infants is accompanied by a low heart rate variability (HRV). It was found that electroencephalographic bursts of slow waves during TA are coupled with an acceleration of the HR. In this study, this synchronous behaviour of EEG bursts and HR is described for the first time in a group of preterm infants with a mean conceptional age (CA) of 36 weeks.

Journal ArticleDOI
TL;DR: It was found that spontaneous activity transients or slow wave EEG bursts during "Tracé alternant" (TA) can be accompanied by an HR acceleration of 1-2% and evidence of a coherent behaviour of EEG bursts and HR in the developing nervous system of preterm infants is given.

Journal ArticleDOI
TL;DR: Application of this stimulation system enabled the patient to drink for the first time after the accident from a glass without any additional help.
Abstract: 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

21 Sep 2005
TL;DR: In this article, a brain-computer interface (BCI) is used to transform thought-modulated EEG signals into an output signal that controls events within a virtual environment (VE).
Abstract: 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.



Journal ArticleDOI
TL;DR: A method for automatic detection of slow wave EEG-bursts and a tool to average changes in the EEG and the corresponding heart rate to be comparable to the results of an expert.
Abstract: 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.

Journal ArticleDOI
TL;DR: The aim of this study was to restore the grasp function of a tetraplegic patient with a C5 spinal cord injury by means of functional electrical stimulation (FES), and a simple palmar grasp was realised.
Abstract: 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.

Proceedings ArticleDOI
16 Mar 2005
TL;DR: 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.
Abstract: 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

01 Jan 2005
TL;DR: Heart rate responses induced by motor imagery were investigated in 4 subjects in a series of experiments with a Brain-Computer Interface (BCI) and thought-based control of VE resulted in a heart rate increase in 2 subjects and aheart rate decrease in the other 2 subjects.
Abstract: 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.

Journal ArticleDOI
TL;DR: In this article, the Greiffunktion mittels funktioneller Elektrostimulation (FES) wiederherzustellen wurde entweder mit einem Schalter am Rollstuhl oder erstmalig with einem EEG-basierten Brain-Computer-Interface (BCI) gesteuert.
Abstract: Ziel dieser Arbeit war es, bei einem Patienten mit kompletter C5-Tetraplegie die Greiffunktion mittels funktioneller Elektrostimulation (FES) wiederherzustellen. Mit 3 Paar Oberflachenelektroden 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 moglich, selbstandig und ohne fremde Hilfe aus einem Glas zu trinken.