Biomedical subjects

G Pfurtscheller

Publications and source records attributed to G Pfurtscheller.

At least 19 recordsLinked to original sources

PubMed2006

A fully automated correction method of EOG artifacts in EEG recordings.

OBJECTIVE: A fully automated method for reducing EOG artifacts is presented and validated. METHODS: The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in a blinded evaluation. RESULTS: The expert scorers identified in 5.9% of the raw data some EOG artifacts; 4.7% were corrected. After applying the EOG correction, the expert scorers identified in another 1.9% of the data some EOG artifacts, which were not recognized in the uncorrected data. CONCLUSIONS: The advantage of a fully automated reduction of EOG artifacts justifies the small additional effort of the proposed method and is a viable option for reducing EOG artifacts. The method has been implemented for offline and online analysis and is available through BioSig, an open source software library for biomedical signal processing. SIGNIFICANCE: Visual identification and rejection of EOG-contaminated EEG segments can miss many EOG artifacts, and is therefore not sufficient for removing EOG artifacts. The proposed method was able to reduce EOG artifacts by 80%.

Adolescent↗

PubMed2006

Seperability of four-class motor imagery data using independent components analysis.

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.

Adult↗

PubMed2006

Cardiac responses induced during thought-based control of a virtual environment.

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). The cue specified right hand vs. leg/foot motor imagery. After a number of BCI training sessions reaching a classification accuracy of at least 80%, the BCI experiments were carried out in an immersive virtual environment (VE), commonly referred as a "CAVE". In this VE, the subjects were able to move along a virtual street by motor imagery alone. The thought-based control of VE resulted in an acceleration of the heart rate in 2 subjects and a heart rate deceleration in the other subject. In control experiments in front of a PC, all 3 subjects displayed a significant heart rate deceleration of the order of about 3-5%. This heart rate decrease during motor imagery in a normal environment is similar to that observed during preparation for a voluntary movement. The heart rate acceleration in the VE is interpreted as effect of an increased mental effort to walk as far as possible in VE.

Adult↗

PubMed2006

Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks.

We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz +/- 0.4 (mean +/- SD). While the desynchronized components were broad banded and centered at 10.9 Hz +/- 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz +/- 1.0. 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. This implies that such EEG phenomena may be utilized in a multi-class brain-computer interface (BCI) operated simply by motor imagery.

Adult↗

PubMed2006

A fully on-line adaptive BCI.

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.

Algorithms↗

PubMed2006

15 years of BCI research at Graz University of Technology: current projects.

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.

Animals↗

PubMed2005

Beta rebound after different types of motor imagery in man.

We investigated the behavior of short-lasting beta bursts (beta rebound, beta ERS) induced after imagination of hand, foot or tongue movement. Nine able-bodied subjects were asked to imagine one type of movement following the presentation of a visual cue stimulus. EEG was recorded from 60 closely spaced electrodes placed over frontal, central and parietal areas. Spatiotemporal ERD/ERS patterns were evaluated in form of time-frequency maps. Seven out of nine subjects displayed a significant short-lasting beta power increase (beta rebound) after termination of imagery of movement of both feet in the frequency range of 23-29Hz with a clear maximum at the vertex. In contrast, only two subjects displayed a clear beta rebound at the vertex after imagery of one of the hands, although five exhibited a significant beta rebound at the contralateral side of the head. None showed a beta rebound after tongue motor imagery. 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.

Adult↗

PubMed2005

[Functional electrical stimulation instead of surgery? Improvement of grasping function with FES in a patient with C5 tetraplegia].

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.

Activities of Daily Living↗

PubMed2005

Exploring virtual environments with an EEG-based BCI through motor imagery.

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.

Adult↗

PubMed2005

Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates.

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 naïve 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.

Algorithms↗

PubMed2005

Propagation of EEG activity in the beta and gamma band during movement imagery in humans.

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.

Adolescent↗

PubMed2004

Brain-computer communication based on the dynamics of brain oscillations.

This chapter presents a review of brain-computer communication based on motor imagery and the dynamics of brain oscillations. The concept of motor imagery as experimental strategy and the two different modes of operation a brain-computer interface can have are explained. An EEG based brain switch that can control a FES-induced hand grasp of a tetraplegic and an approach towards an ECoG based brain switch are presented.

Brain↗

PubMed2003

Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man.

Event-related beta electroencephalographic (EEG) changes were studied during wrist movements induced by functional electrical stimulation (FES) of the appropriate forearm muscles in healthy volunteers. Active and passive hand movements were investigated as control conditions. Significant EEG changes with respect to a pre-movement period were analyzed by calculating time-frequency maps of event-related (de-)synchronization (ERD/ERS) for 32 EEG channels recorded from sensorimotor and premotor areas. Immediately after the beginning of the FES movement, a prominent ERD was found, followed by a beta ERS similar to that observed after active or passive wrist movements. Both changes were maximal over the contralateral primary hand area. The main difference between active and stimulation-induced movements was that in the latter case no ERD was detectable prior to movement-onset. These findings suggest that the sensorimotor processing during FES involves some of the processes which are also involved in voluntary hand movements.

Adult↗

PubMed2003

Early onset of post-movement beta electroencephalogram synchronization in the supplementary motor area during self-paced finger movement in man.

A voluntary finger movement is accompanied by an event-related desynchronization followed by a short burst of beta oscillations or event-related synchronization. These beta bursts are dominant over the contralateral hand representation area, but also appear over the midcentral area overlaying the supplementary motor area (SMA) and the foot representation area. We show that the induced midcentral beta oscillations following movement-offset display not only slightly higher frequency components, but have also a significantly earlier onset. These beta oscillations arise likely from the SMA. Assuming that the short-lasting beta synchronizations at frequencies below 35 Hz after termination of a movement reflect a state of localized cortical inhibition, we propose that the induced midcentral oscillations reflect the inhibition of networks within the SMA. This assumed resetting or inhibitory process within the SMA precedes that of the networks within the primary motor hand area.

Beta Rhythm↗

PubMed2003

Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment.

OBJECTIVE: This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication. METHODS: EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS). RESULTS: The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min. CONCLUSIONS: The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.

Adult↗

PubMed2003

Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement.

OBJECTIVE: To study the spatiotemporal pattern of event-related desynchronization (ERD) and event-related synchronization (ERS) in electrocorticographic (ECoG) data with closely spaced electrodes. METHODS: Four patients with epilepsy performed self-paced hand movements. The ERD/ERS was quantified and displayed in the form of time-frequency maps. RESULTS: In all subjects, a significant beta ERD with embedded gamma ERS was found. CONCLUSIONS: Self-paced movement is accompanied not only by a relatively widespread mu and beta ERD, but also by a more focused gamma ERS in the 60-90 Hz frequency band.

Adult↗

PubMed2003

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.

Adult↗

PubMed2003

[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.

Action Potentials↗