Biomedical subjects

Gernot R Müller-Putz

Publications and source records attributed to Gernot R Müller-Putz.

10 recordsLinked to original sources

PubMed2006

Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients.

A number of electroencephalographic (EEG) studies report on motor event-related desynchronization and synchronization (ERD/ERS) in the beta band, i.e. a decrease and increase of spectral amplitudes of central beta rhythms in the range from 13 to 35 Hz. Following an ERD that occurs shortly before and during the movement, bursts of beta oscillations (beta ERS) appear within a 1-s interval after movement offset. Such a post-movement beta ERS has been reported after voluntary hand movements, passive movements, movement imagination, and also after movements induced by functional electrical stimulation. The present study compares ERD/ERS patterns in paraplegic patients (suffering from a complete spinal cord injury) and healthy subjects during attempted (active) and passive foot movements. The aim of this work is to address the question, whether patients do have the same focal beta ERD/ERS pattern during attempted foot movement as healthy subjects do. The results showed midcentral-focused beta ERD/ERS patterns during passive, active, and imagined foot movements in healthy subjects. This is in contrast to a diffuse and broad distributed ERD/ERS pattern during attempted foot movements in patients. Only one patient showed a similar ERD/ERS pattern. Furthermore, no significant ERD/ERS patterns during passive foot movement in the group of the paraplegics could be found.

Adolescent↗

PubMed2006

Post-movement beta synchronization after kinesthetic illusion, active and passive movements.

After the completion of a voluntary movement or in response to somatosensory stimulation, a short-lasting burst of beta oscillations (post movement beta ERS, beta rebound) can be observed. In the present study, we investigated if this is also true for the illusion of movements, induced by a vibration at 80 Hz on the biceps tendon. We compared the post-movement synchronization of EEG beta rhythms induced by active and passive movements and illusion in eight right-handed healthy subjects. As a result, a short-lasting post-movement beta ERS was present over motor areas after both active and passive and also after illusion of movement in all subjects. These results suggested a possible role of MI and the somatosensory cortex in the somatic perception of limb movement in humans.

Adult↗

PubMed2006

Motor imagery and EEG-based control of spelling devices and neuroprostheses.

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.

Communication Devices for People with Disabilities↗

PubMed2006

Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?

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.

Adult↗

PubMed2006

Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation.

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.

Adult↗

PubMed2005

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.

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%.

Adult↗

PubMed2005

Relationship between slow-wave EEG bursts and heart rate changes in preterm infants.

The objective of the study is to explore interactions between cortical and autonomic functions in the first weeks of postnatal life. We investigated the behaviour of one-channel electroencephalogram (EEG) patterns and heart rate (HR) dynamics in preterm infants. In a group of 15 healthy preterm infants with a mean conceptional age (CA) of 36 weeks and a mean postnatal age of 17.5 days simultaneous registration of amplitude integrated EEG (aEEG) and HR pattern was performed during interfeeding intervals. Periods with a discontinuous EEG activity and a low heart-rate variability (HRV) were selected for further processing and averaging. It was found that spontaneous activity transients (SATs) or slow wave EEG bursts during "Tracé alternant" (TA) can be accompanied by an HR acceleration of 1-2% (mean: 1.9, range: 0.6-3.5 beats/min) lasting approximately 5s (mean, range: 3.6-7.1s). The aim of the study is to give evidence of a coherent behaviour of EEG bursts and HR in the developing nervous system of preterm infants.

Action Potentials↗

PubMed2005

Synchronous occurrence of EEG bursts and heart rate acceleration in preterm infants.

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 'Tracé 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.

Autonomic Nervous System↗

PubMed2005

EEG-based neuroprosthesis control: a step towards clinical practice.

This case study demonstrates the coupling of an electroencephalogram (EEG)-based Brain-Computer Interface (BCI) with an implanted neuroprosthesis (Freehand system). Because the patient was available for only 3 days, the goal was to demonstrate the possibility of a patient gaining control over the motor imagery-based Graz BCI system within a very short training period. By applying himself to an organized and coordinated training procedure, the patient was able to generate distinctive EEG-patterns by the imagination of movements of his paralyzed left hand. These patterns consisted of power decreases in specific frequency bands that could be classified by the BCI. The output signal of the BCI emulated the shoulder joystick usually used, and by consecutive imaginations the patient was able to switch between different grasp phases of the lateral grasp that the Freehand system provided. By performing a part of the grasp-release test, the patient was able to move a simple object from one place to another. The results presented in this work give evidence that Brain-Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions. 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.

Adult↗

PubMedDate not supplied

[Semiautomatic procedure for the investigation of synchronized activity of EEG and heart rate--examination of preterm births].

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.

Algorithms↗