Showing papers by "Gert Pfurtscheller published in 2006"
TL;DR: The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event- related synchronization (ERS) patterns were induced in at least one or two tasks.
1,402 citations
19 Jun 2006
TL;DR: The third BCI Competition to address several of the most difficult and important analysis problems in BCI research is organized and the paper describes the data sets that were provided to the competitors and gives an overview of the results.
Abstract: A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.
814 citations
TL;DR: Findings support that interfering sensorimotor activation and deactivation is reflected in graduated changes of induced mu and beta oscillations.
Abstract: Oscillations in the alpha and beta band (<35 Hz) show characteristic spatiotemporal patterns during sensorimotor processing. Whereas event-related desynchronization (ERD) during motor preparation, execution, and imagery can be seen as a correlate of an activated cortical area, event-related synchronization (ERS) of frequency components between 10 and 13 Hz may represent a deactivated cortical area or inhibited cortical network, at least under certain conditions. Induced beta rhythms (13–35 Hz, beta ERS) can be found in sensorimotor areas following both voluntary movement and somatosensory stimulation. In a recent study we used different tasks involving execution and imagery of movements of the upper and lower limb to produce activation vs. deactivation/inhibition of the sensorimotor hand area. Sensorimotor interference, as a function of the activation level of the motor cortex, was studied by the use of repetitive median nerve stimulation (MNS) (ISI 1.5 s) in 12 healthy volunteers during the following task conditions: (i) cube manipulation between thumb and fingers of one hand, (ii) imagined cube manipulation, (iii) continuous foot rotation movements, and (iv) imagined foot movements. EEG was recorded from hand and foot representation areas and processed time-locked to MNS (ERD/ERS). In addition, task-related band power changes (TRPD/TRPI) were analyzed. We found a clear-cut suppression of the stimulation-induced beta ERS (indicating an enhanced activity state of the sensorimotor areas) during active cube manipulation and a weaker suppression during cube imagery. Mental imagination of foot movement led to an increase of the hand area mu rhythm, but did not interfere with stimulation-related effects on beta ERS. These findings support that interfering sensorimotor activation and deactivation is reflected in graduated changes of induced mu and beta oscillations.
662 citations
13 Mar 2006
TL;DR: Evidence is given that it is possible to set up a BCI which is based on SSSEPs, andducers have been used for the stimulation of both index fingers using tactile stimulation in the "resonance"-like frequency range of the somatosensory system.
Abstract: One of the main issues in designing a brain-computer interface (BCI) is to find brain patterns, which could easily be detected. One of these pattern is the steady-state evoked potential (SSEP). SSEPs induced through the visual sense have already been used for brain-computer communication. In this work, a BCI system is introduced based on steady-state somatosensory evoked potentials (SSSEPs). Transducers have been used for the stimulation of both index fingers using tactile stimulation in the "resonance"-like frequency range of the somatosensory system. Four subjects participated in the experiments and were trained to modulate induced SSSEPs. Two of them learned to modify the patterns in order to set up a BCI with an accuracy of between 70% and 80%. Results presented in this work give evidence that it is possible to set up a BCI which is based on SSSEPs.
279 citations
TL;DR: Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components, however, the performance of Laplacian derivations was comparable withinfomax for both cross-validated and unseen data.
Abstract: This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on different days were recorded for eight subjects. Three different independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) were studied and compared to common spatial patterns (CSP), Laplacian derivations and standard bipolar derivations, which are other well-known preprocessing methods. Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components. However, the performance of Laplacian derivations was comparable with Infomax for both cross-validated and unseen data. The overall best four-class classification accuracies (between 33% and 84%) were obtained with CSP. For the cross-validated training data, CSP performed slightly better than Infomax, whereas for unseen test data, CSP yielded significantly better classification results than Infomax in one of the sessions.
271 citations
TL;DR: Online analysis and classification of single electroencephalogram (EEG) trials during motor imagery were used for navigation in the virtual environment (VE) to demonstrate for the first time that it is possible to move through a virtual street without muscular activity when the participant only imagines feet movements.
244 citations
TL;DR: The basic methods used in Graz-BCI research are described and possible clinical applications are outlined, which can be used to assist patients who have highly compromised motor functions.
Abstract: A brain-computer interface (BCI) transforms signals originating from the human brain into commands that can control devices or applications. With this, a BCI provides a new non-muscular communication channel, which can be used to assist patients who have highly compromised motor functions. The Graz-BCI uses motor imagery and associated oscillatory EEG signals from the sensorimotor cortex for device control. As a result of research in the past 15 years, the classification of ERD/ERS patterns in single EEG trials during motor execution and motor imagery forms the basis of this sensorimotor-rhythm controlled BCI. The major frequency bands of cortical oscillations considered here are the 8-13 and 15-30 Hz bands. This chapter describes the basic methods used in Graz-BCI research and outlines possible clinical applications.
200 citations
TL;DR: A viable fully on-line adaptive brain computer interface (BCI) is introduced that was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier was an adaptive quadratic discriminant analysis.
Abstract: A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.
194 citations
TL;DR: According to current therapeutic strategies, feedback-regulated motor imagery could be used to enhance antagonistic ERD/ERS patterns and therewith, support activation of the stroke affected and inhibition of the non-affected, contralesional hemisphere.
Abstract: ERD/ERS patterns characterize the dynamics of brain oscillations time-locked but not phase-locked to an externally or internally triggered event. Recent studies have shown that ERD/ERS phenomena in narrow frequency bands are remarkably stable over time and across different testing situations. The high reproducibility of ERD/ERS promotes the usefulness of this biometric measure in assessing individual characteristics. In addition to the spatio-temporal patterns of (de)synchronization processes the most reactive frequency components are especially highly subject-specific and, therefore, open up new possibilities for user authentication and person identification. In contrast, ERD/ERS research will continue to be useful in clinical brain–computer interface (BCI) implementation. Promising novel applications of an ERD/ERS based BCI may contribute to enhanced functional recovery and rehabilitation in patients suffering from chronic stroke. According to current therapeutic strategies, feedback-regulated motor imagery could be used to enhance antagonistic ERD/ERS patterns and therewith, support activation of the stroke affected and inhibition of the non-affected, contralesional hemisphere.
182 citations
19 Jun 2006
TL;DR: Over the last 15 years, the Graz Brain-Computer Interface has been developed and all components such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated.
Abstract: Over the last 15 years, the Graz Brain-Computer Interface (BCI) has been developed and all components such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. Recent projects deal with the development of asynchronous BCIs, the presentation of feedback and applications for communication and control.
172 citations
TL;DR: The study shows that the heart rate and heart rate variability parameters vary significantly between the training and experimental phase and there were also differences in response between participants who report more or less socially anxious.
Abstract: An experiment was conducted in a Cave-like environment to explore the relationship between physiological responses and breaks in presence and utterances by virtual characters towards the participants. Twenty people explored a virtual environment (VE) that depicted a virtual bar scenario. The experiment was divided into a training and an experimental phase. During the experimental phase breaks in presence (BIPs) in the form of whiteouts of the VE scenario were induced for 2 s at four equally spaced times during the approximately 5 min in the bar scenario. Additionally, five virtual characters addressed remarks to the subjects. Physiological measures including electrocardiagram (ECG) and galvanic skin response (GSR) were recorded throughout the whole experiment. The heart rate, the heart rate variability, and the event-related heart rate changes were calculated from the acquired ECG data. The frequency response of the GSR signal was calculated with a wavelet analysis. The study shows that the heart rate and heart rate variability parameters vary significantly between the training and experimental phase. GSR parameters and event-related heart rate changes show the occurrence of breaks in presence. Event-related heart rate changes also signified the virtual character utterances. There were also differences in response between participants who report more or less socially anxious.
TL;DR: This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV), and reveals that all subjects were able to control three mental states.
Abstract: Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence suggests that additional information is obtained by quantifying the relationship between signals of single electrodes, which might provide innovative features for future BCI systems. This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV). In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session
TL;DR: This chapter addresses the visualization of these phenomena and the validation of the results through statistical significance testing and reports on preprocessing using independent component analysis (ICA) and introduces a novel ERD/ERS maximization method.
Abstract: In this chapter we review the traditional approach for ERD/ERS quantification and a more recent approach based on wavelet transform. In particular, we address the visualization of these phenomena and the validation of the results through statistical significance testing. Furthermore, we report on preprocessing using independent component analysis (ICA) and introduce a novel ERD/ERS maximization method.
TL;DR: The goals of this work are to show the influence of different feedback types on the same task, and to demonstrate that it is possible to move through a VE without any muscular activity, using only the imagination of foot movement.
Abstract: Healthy participants are able to move forward within a virtual environment (VE) by the imagination of foot movement. This is achieved by using a brain-computer interface (BCI) that transforms thought-modulated electroencephalogram (EEG) recordings into a control signal. A BCI establishes a communication channel between the human brain and the computer. The basic principle of the Graz-BCI is the detection and classification of motor-imagery-related EEG patterns, whereby the dynamics of sensorimotor rhythms are analyzed. A BCI is a closed-loop system and information is visually fed back to the user about the success or failure of an intended movement imagination. Feedback can be realized in different ways, from a simple moving bar graph to navigation in VEs.The goals of this work are twofold: first, to show the influence of different feed-back types on the same task, and second, to demonstrate that it is possible to move through a VE (e.g., a virtual street) without any muscular activity, using only the imagination of foot movement. In the presented work, data from BCI feedback displayed on a conventional monitor are compared with data from BCI feedback in VE experiments with a head-mounted display (HMD) and in a high immersive projection environment (Cave). Results of three participants are reported to demonstrate the proof-of-concept. The data indicate that the type of feedback has an influence on the task performance, but not on the BCI classification accuracy. The participants achieved their best performances viewing feedback in the Cave. Furthermore the VE feedback provided motivation for the subjects.
TL;DR: A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results in the offline analysis of four-class brain-computer interface (BCI) data recordings.
Abstract: We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future. A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results. As reference methods we used traditional band power features and the method of common spatial patterns. We consider also for the first time in the context of a four-class problem the issue of variability of the features over time and how much data is required to give good classification results. We do this in a practical way where training data precedes testing data in time.
TL;DR: The future potential of BCI methods for various control purposes, especially for functional rehabilitation of tetraplegics using neuroprosthetics, is outlined and the general steps from a synchronous or cue-guided BCI to an internally driven asynchronous brain-switch are discussed.
Abstract: Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design. Based on the results of two clinical applications, where 'thought' control of neuroprostheses based on movement imagery in tetraplegic patients with a high spinal cord injury has been established, the general steps from a synchronous or cue-guided BCI to an internally driven asynchronous brain-switch are discussed. The future potential of BCI methods for various control purposes, especially for functional rehabilitation of tetraplegics using neuroprosthetics, is outlined.
TL;DR: The Cortical Activation Model is an attempt to explain whether an internally or externally paced event reveals an event-related desynchronization (ERD) or event- related synchronization (ERS) in a specific frequency band.
Abstract: The Cortical Activation Model (CAM) is an attempt to explain whether an internally or externally paced event reveals an event-related desynchronization (ERD) or event-related synchronization (ERS) in a specific frequency band. It is assumed that the amplitude of network-specific oscillations depends on, in addition to other factors, the number of neurons available for synchronization and the excitability level of neurons and forms a bell-shaped curve with a maximum of oscillatory activity at a certain balance of both factors. Depending on the baseline level of cortical activation (CA) and the location of the "working point" (WP), a sudden change of activation can induce either ERD or ERS in a given area.
TL;DR: A possible role of MI and the somatosensory cortex in the somatic perception of limb movement in humans is suggested.
TL;DR: Cardiac responses induced by motor imagery were investigated in 3 subjects in a series of experiments with a synchronous (cue-based) Brain-Computer Interface (BCI), which resulted in an acceleration of the heart rate in 2 subjects and a heart rate deceleration in the other subject.
14 Apr 2006
TL;DR: The current approaches and methods used in BCI research are outlined, with emphasis on the signal processing part of the system, consisting of preprocessing, feature extraction, and classification.
Abstract: A brain computer interface (BCI) transforms electrophysiological signals originating from the human brain into commands that control devices or applications. In this way, a BCI provides a new nonmuscular communication channel, which can be extremely useful for people with severe neuromuscular disorders such as amyotrophic lateral sclerosis and brainstem stroke. The immediate goal of our current research is to provide these users with an opportunity to communicate with their environment. Future applications also include the field of multimedia and virtual reality.
Present day BCIs use a variety of electrophysiological signals such as slow cortical potentials, evoked potentials (P300), oscillatory activity recorded from scalp or subdural electrodes, and cortical neuronal activity recorded from implanted electrodes. EEG is by far the most frequently used input source, because it is readily available and noninvasive. The poor signal-to-noise ratio of scalp–recorded signals requires the application of advanced signal processing methods.
This article outlines and explains the current approaches and methods used in BCI research with emphasis on the signal processing part of the system, consisting of preprocessing, feature extraction, and classification. However, the success of a BCI depends not only on the methodologies applied, but also on the capability of the user to develop and maintain the skill to produce the brain patterns employed by the BCI. Therefore, the interaction between the BCI system and the user, in terms of adaptation and learning, is a challenging aspect of any BCI development and application.
Keywords:
brain-computer interface;
EEG;
signal processing;
pattern recognition;
feature extraction
01 Jan 2006
01 Jan 2006
01 Jan 2006
TL;DR: A high spinal cord injured tetraplegic patient was able to generate bursts of beta oscillations in the EEG by imagination of foot movements by using asynchronously to control a virtual environment (VE).
Abstract: A high spinal cord injured tetraplegic patient was able to generate bursts of beta oscillations in the EEG by imagination of foot movements [1]. Only one single EEG channel was analyzed and classified sample-by-sample by a Brain-Computer Interface (BCI) and used asynchronously to control a virtual environment (VE). The used VE was a virtual street populated with 15 avatars [2]. The patient was placed with his wheelchair in the middle of a 4-wall-projection CAVE and his task was to “walk” towards the end of the virtual street by movement imagination of his paralyzed feet. Every time he was passing by an avatar he had to stop very close to it. The avatar started talking to the patient if he was standing still for one second. After a while, by free will, the subject could imagine the next foot movement and started walking again, till the end of the street was reached.
01 Jan 2006
01 Oct 2006
TL;DR: In this sturdy, feature extraction based on Directed Information analysis is introduced to discriminate the EEG signals recorded during the right hand, the left hand and the right foot motor imagery.
Abstract: Electroencephalograph (EEG) recordings during the right and the left hand 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 eg, handicap users with amyotrophic lateral sclerosis (ALS) The conventional method purposes the recognition of the right hand and the left hand motor imagery In this study, statistical pattern recognition method based on AR model is introduced to discriminate the EEG signals recorded during the right hand, the left hand and the right foot motor imagery And the recognition of the hand motor imagery and the foot motor imagery also is carried out Finally, the effectiveness of our method is confirmed through the experimental studies
06 Sep 2006
01 Jan 2006
21 Sep 2006