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Biomedical subjects

G Pfurtscheller

Publications and source records attributed to G Pfurtscheller.

At least 55 records · Page 3Linked to original sources
PubMed2000

Do brain oscillations of different frequencies indicate interaction between cortical areas in humans?

Oscillatory cortical activity in the beta frequency range (14-32 Hz) was investigated in a voluntary movement task using closely-spaced electroencephalogram recordings over the sensorimotor cortex. According to recent studies, short-lasting beta oscillations were observed within the first second after termination of a self-paced finger movement. In this study we show, moreover, that finger movement creates beta bursts not only overlying the hand representation area, but also, at a higher frequency, over neighboring cortical areas representing the foot. The results indicate that, related to the same event, distinct cortical areas can display increased synchronized activity at different frequencies, providing evidence for interaction between the corresponding neuronal networks.

Action Potentials↗
PubMed2000

Classification of movement-related EEG in a memorized delay task experiment.

OBJECTIVES: We studied the activation of cortical motor areas during a memorized delay task with a classification technique. METHODS: Multichannel EEG was recorded during the sequence of warning stimulus, visual cue, reaction stimulus, and actual execution of hand or foot movements. Two different approaches are presented: first, we trained a classifier on data from the time segments immediately preceding the actual movements, and analyzed the whole recordings in overlapping segments with this fixed classifier. The classification rates obtained as a function of experimental time reflect the activation of the same cortical areas that are active during the actual movements. In the second approach, we trained classifiers on data segments with the same latency in time as the data tested ('running classifiers'). By this, we checked whether we could detect event-related activity sufficiently marked to allow for correct classification. RESULTS: With the fixed classifier approach we found two maxima of classification: one maximum after processing of the visual cue corresponding to an activation of motor cortex without overt movement, and a second maximum at the time of the actual movement. The first maximum relates to a very short-lived brain state, in the order of 300 ms, while the broad second maximum (1.5 s) indicates a very stable and long-lasting activation. CONCLUSIONS: With the running classifier approach we found similar maxima as with the fixed classifier, indicating that only the activity of motor areas is relevant for classification. Possible implications of our findings for the development of a brain computer interface (BCI) are discussed.

Acoustic Stimulation↗
PubMed2000

Functional dissociation of lower and upper frequency mu rhythms in relation to voluntary limb movement.

OBJECTIVE: The goal of this study is to investigate the reactivity of central rhythms in the alpha band during self-paced voluntary finger and foot movement and to give an answer to the question, whether different types of mu rhythms exist. METHODS: The effect of self-paced, voluntary finger and foot movement was studied in a group of 12 right-handed healthy volunteers. The EEG was recorded from a grid of 34 electrodes placed over sensorimotor areas with inter-electrode distances of approximately 2.5 cm. The event-related desynchronization (ERD) was quantified in the 8-10 and 10-12 Hz bands. RESULTS: Both frequency components are blocked prior to and during movement and therefore, they have to be considered as mu rhythms. The lower frequency component results in a widespread movement-type non-specific ERD pattern, whereas the upper frequency component shows a more focused and movement-type specific pattern, clearly different with finger and foot movement. CONCLUSIONS: The distinct reactivity patterns provide evidence for the existence of two types of mu rhythms, a somatotopically non-specific lower frequency mu rhythm and a somatotopically specific mu rhythm characteristically found in the upper alpha frequency band.

Adult↗
PubMed2000

EEG-based communication: presence of an error potential.

BACKGROUND: EEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect. METHODS: In an effort to develop a method for detecting errors, this study compared the EEG immediately after correct target selection to that after incorrect selection. RESULTS: The data showed that a mistake is followed by a positive potential centered at the vertex that peaks about 180 ms after the incorrect selection. CONCLUSION: The results suggest that this error potential might provide a method for detecting and voiding errors that requires no additional time and could thereby improve the speed and accuracy of EEG-based communication.

Adult↗
PubMed2000

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.

Alpha Rhythm↗
PubMed2000

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.

Adult↗
PubMed2000

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.

Adolescent↗
PubMed2000

Using time-dependent neural networks for EEG classification.

This paper compares two different topologies of neural networks. They are used to classify single trial electroencephalograph (EEG) data from a brain-computer interface (BCI). A short introduction to time series classification is given, and the used classifiers are described. Standard multilayer perceptrons (MLPs) are used as a standard method for classification. They are compared to finite impulse response (FIR) MLPs, which use FIR filters instead of static weights to allow temporal processing inside the classifier. A theoretical comparison of the two architectures is presented. The results of a BCI experiment with three different subjects are given and discussed. These results demonstrate the higher performance of the FIR MLP compared with the standard MLP.

Cerebral Cortex↗
PubMed1999

Lack of bilateral coherence of post-movement central beta oscillations in the human electroencephalogram.

Voluntary finger movement results in attenuation or desynchronization of the Rolandic mu and central beta rhythms some seconds before movement, followed by a post-movement synchronization of the central beta activity (post-movement beta synchronization). Although it has been demonstrated that the Rolandic mu rhythms show a lack of bilateral coherence, the coherence between post-movement central beta oscillations over left and right hemispheres has, as yet, not been investigated. In this study, post-movement changes in central beta activity over left and right sensorimotor hand areas are investigated in 13 healthy subjects by calculation of event-related coherence (ERCoh). The ERCoh time course allows the investigation of linear phase-coupling before, during and after voluntary movement. It is shown that no interhemispheric coherence exists between post-movement beta oscillations.

Aged↗
PubMed1999

Visually guided motor imagery activates sensorimotor areas in humans.

Stimulus-related changes in ongoing electroencephalography (EEG) over sensorimotor areas were investigated during a visually cued motor imagery task. Four subjects were instructed to imagine one-sided hand movements in response to visual cue stimuli. The EEG was recorded from central areas using 27 electrodes set at distances of 2.5 cm. The method of common spatial filters was used to extract discriminatory information of EEG patterns recorded during the two motor imagery conditions. Single EEG trials were classified in intervals of 250 ms for a 8-s period starting 3 s prior to stimulus presentation. The results suggest that perception of the visual cue stimulus modifies oscillations in sensorimotor areas specific to the indicated hand starting as soon as 250-500 ms after stimulus onset.

Brain↗
PubMed1999

Human movement-related potentials vs desynchronization of EEG alpha rhythm: a high-resolution EEG study.

Movement-related potentials (MRPs) and event-related desynchronization (ERD) of alpha rhythm were investigated with an advanced high-resolution electroencephalographic technology (128 channels, surface Laplacian estimate, realistic head modeling). The working hypothesis was that MRPs and alpha ERD reflect different aspects of sensorimotor cortical processes. Both MRPs and alpha ERD modeled the responses of primary sensorimotor (M1-S1), supplementary motor (SMA), and posterior parietal (PP, area 5) areas during the preparation and execution of unilateral finger movements. Maximum responses were modeled in the contralateral M1-S1 during both preparation and execution of the movement. The SMA and PP responses were modeled mainly from the MRPs and alpha ERD, respectively. The modeled ipsilateral M1-S1 responses were larger and stronger in the alpha ERD than MRPs. These results may suggest that alpha ERD reflects changes in the background oscillatory activity in wide cortical sensorimotor areas, whereas MRPs represent mainly increased, task-specific responses of SMA and contralateral M1-S1.

Adult↗
PubMed1999

Designing optimal spatial filters for single-trial EEG classification in a movement task.

We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multichannel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain-computer interface.

Brain↗
PubMed1999

Event-related EEG/MEG synchronization and desynchronization: basic principles.

An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.

Cortical Synchronization↗
PubMed1999

Quality control of polysomnographic sleep data by histogram and entropy analysis.

OBJECTIVE AND METHODS: Sixteen polysomnographic recordings from 8 European sleep laboratories were analyzed. The histogram analysis was used to introduce quality control of all-night EEG recordings. RESULTS: It was found that the header information does not always provide the real saturation values of the recording equipment. The entropy measure was used for the quantitative analysis of the dynamic range of routinely used polysomnographic recorders. It was found that the recording equipment provides EEG data with entropy in the range of 8-11 bits. CONCLUSION: In the all-night sleep EEG were observed non-linearities. It is recommended that the equipment provide the saturation values in order to apply automated overflow detection.

Humans↗
PubMed1999

Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery.

EEG feedback studies demonstrate that human subjects can learn to regulate electrocortical activity over the sensorimotor cortex. Such self-induced EEG changes could serve as control signals for a Brain Computer Interface. The experimental task of the current study was to imagine either right-hand or left-hand movement depending on a visual cue stimulus on a computer monitor. The performance of this imagination task was controlled on-line by means of a feedback bar that represented the current EEG pattern. EEG signals recorded from left and right central recording sites were used for on-line classification. For the estimation of EEG parameters, an adaptive autoregressive model was applied, and a linear discriminant classifier was used to discriminate between EEG patterns associated with left and right motor imagery. Four trained subjects reached 85% to 95% classification accuracy in the course of the experimental sessions. To investigate the impact of continuous feedback presentation, time courses of band power changes were computed for subject-specific frequency bands. The EEG data revealed a significant event-related desynchronization over the contralateral central area in all subjects. Two subjects simultaneously displayed synchronization of EEG activity (event-related synchronization) over the ipsilateral side. During feedback presentation the event-related desynchronization/event-related synchronization patterns showed increased hemispheric asymmetry compared to initial control sessions without feedback.

Adult↗
PubMed1999

Functional imaging of postmovement beta event-related synchronization.

The linear estimation (LE) and spline surface Laplacian (SL) method were applied to single-trial EEG data. EEG was recorded in three subjects during voluntary, self-paced movements of the index finger. The EEG data were bandpass-filtered in the lower beta frequency range and showed short-lasting bursts of oscillations after termination of movement. These oscillations are termed postmovement beta synchronization. The realistic head geometry and the digitized positions of the electrodes were taken into account for accurate modeling of the anatomy. Regularization of the LE method was achieved by the truncated singular value decomposition. The LE and SL distribution of the postmovement beta synchronization showed similar spatial and temporal patterns. A clear increase of the LE source activity was found over the primary motor area. These results indicate that the postmovement beta synchronization is generated over the anterior bank of the central sulcus.

Brain↗
PubMed1999

Event-Related changes of band power and coherence: methodology and interpretation.

Event-related calculation of band power changes can be used to quantify event-related desynchronization, event-related synchronization, and event-related coherence (ERCoh). It is shown that in the case of a motor task especially, the ERCoh time course depends on the type of EEG derivation used, whereby referenced EEG data can result in a bilateral coherence increase, although both hemispheres generate independent sensorimotor rhythms. It is further shown that not only Rolandic mu rhythms but also central beta rhythms display a lack of interhemispheric linear phase coupling.

Alpha Rhythm↗