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OBJECTIVE To study the feasibility of multimodal neuroimaging in mild to moderate Alzheimer disease (AD) and to estimate the size of possible treatment effects of memantine on potential functional, structural and metabolic biomarkers of disease progression. METHODS In this randomised, double-blind, placebo-controlled pilot study, 36 patients with moderate(More)
A brain-computer interface (BCI) can be used to control a limb neuroprosthesis with motor imaginations (MI) to restore limb functionality of paralyzed persons. However, existing BCIs lack a natural control and need a considerable amount of training time or use invasively recorded biosignals. We show that it is possible to decode velocities and positions of(More)
This paper compares classification accuracies of feature extraction methods (FEMs) as used in sensory motor rhythm (SMR) based Brain-Computer Interfaces (BCIs). Features were extracted offline from 9 subjects and classified with linear discriminant analysis. The following FEMs were compared: adaptive autoregressive parameters, band power, phase locking(More)
A brain-computer interface (BCI) can help to overcome movement deficits in persons with spinal-cord injury. Ideally, such a BCI detects detailed movement imaginations, i.e., trajectories, and transforms them into a control signal for a neuroprosthesis or a robotic arm restoring movement. Robotic arms have already been controlled successfully by means of(More)
The consequences of a spinal cord injury (SCI) are tremendous for the patients. The loss of motor functions, especially of grasping, leads to a dramatic decrease in quality of life. With the help of neuroprostheses, the grasp function can be substantially improved in cervical SCI patients. Nowadays, systems for grasp restoration can only be used by patients(More)
In this chapter, we give an overview of the Graz-BCI research, from the classic motor imagery detection to complex movement intentions decoding. We start by describing the classic motor imagery approach, its application in tetraplegic end users, and the significant improvements achieved using coadaptive brain-computer interfaces (BCIs). These strategies(More)
Brain-computer interfaces (BCIs) can detect movement imaginations (MI) which can act as a control signal for a neuroprosthesis of a paralyzed person. However, today's non-invasive BCIs can only detect simply qualities of MI, like what body part is subjected to MI. More advanced future non-invasive BCIs should be able to detect many qualities of MI to allow(More)
The natural control of neuroprostheses is currently a challenge in both rehabilitation engineering and brain-computer interfaces (BCIs) research. One of the recurrent problems is to know exactly when to activate such devices. For the execution of the most common activities of daily living, these devices only need to be active when in the presence of a goal.(More)
A brain-computer interface (BCI) can be used to control a limb neuroprosthesis with motor imaginations (MI) to restore limb functionality in paralyzed persons. However, existing BCIs lack a natural control and need a considerable amount of training time or may use invasively recorded brain signals. A new approach is the direct decoding of movements which(More)
Brain-computer interface (BCI) systems can be used to control limb neuroprostheses in order to restore limb functionality of paralyzed persons. Traditionally, only invasive BMI (brain machine interfaces) are thought to provide an adequate signal-to-noise ratio and bandwidth to control an upper limb neuroprosthesis accurately in a continuous manner with(More)