Technical development and feasibility of a reusable vest to integrate cardiovascular magnetic resonance with electrocardiographic imaging.

Background: Electrocardiographic imaging (ECGI) generates electrophysiological (EP) biomarkers while cardiovascular magnetic resonance (CMR) imaging provides data about myocardial structure, function and tissue substrate. Combining this information in one examination is desirable but requires an affordable, reusable, and high-throughput solution. We therefore developed the CMR-ECGI vest and carried out this technical development study to assess its feasibility and repeatability in vivo.

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI.

Objective: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners.

Approach: To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering.

Effectiveness of Reference Signal-Based Methods for Removal of EEG Artifacts Due to Subtle Movements During fMRI Scanning.

Objective: Subtle motion of an epileptic patient examined with co-registered EEG and functional MRI (EEG-fMRI) may often lead to spurious fMRI activation patterns when true epileptic spikes are contaminated with motion artefacts. In recent years, methods relying on reference signals for correcting these subtle movements in the EEG have emerged. In this study, the performance of two reference-based devices are compared to the template-based method with regard to their ability to remove movement-related artifacts in EEG measured during scanning.

Reduction of EEG artifacts in simultaneous EEG-fMRI: Reference layer adaptive filtering (RLAF).

Although simultaneous measurement of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is one of the most valuable methods for studying human brain activity non-invasively, it remains challenging to measure high quality EEG inside the MRI scanner. Recently, a new approach for minimizing residual MRI scanner artifacts in the EEG was presented: reference layer artifact subtraction (RLAS). Here, reference electrodes capture only the artifacts, which are subsequently subtracted from the measurement electrodes.

How Many People Could Use an SSVEP BCI?

Brain-computer interfaces (BCI) are communication systems that allow people to send messages or commands without movement. BCIs rely on different types of signals in the electroencephalogram (EEG), typically P300s, steady-state visually evoked potentials (SSVEP), or event-related desynchronization. Early BCI systems were often evaluated with a selected group of subjects.

Comparison of dry and gel based electrodes for p300 brain-computer interfaces.

Most brain-computer interfaces (BCIs) rely on one of three types of signals in the electroencephalogram (EEG): P300s, steady-state visually evoked potentials, and event-related desynchronization. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available.

Accuracy of a Brain Computer Interface (P300 spelling device) used by people with motor impairments.

A Brain-Computer Interface (BCI) provides a completely new output pathway and so, an additional possible way a person can express himself if he/she suffers from disorders like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, or other diseases which impair the function of the common output pathways which are responsible for the control of muscles or impair the muscles. Although most BCIs are thought to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on 10 physically disabled people, to the results of a previous study, taken using 100 healthy participants.

How many people are able to control a P300-based brain-computer interface (BCI)?

An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery.

Restless legs syndrome: changes of induced electroencephalographic beta oscillations-an ERD/ERS study.

Study Objectives: Primary or idiopathic restless legs syndrome (RLS) is a sensorimotor disorder of unknown neurophysiologic origin.

Setting And Patients: Ten patients with RLS and 10 healthy control subjects were investigated. Postmovement beta oscillations (event-related synchronization, ERS) induced by movement of the right index finger were measured by electroencephalography and analyzed.