Showing papers by "Gernot Müller-Putz published in 2009"

Discrimination of motor imagery-induced EEG patterns in patients with complete spinal cord injury

Abstract: EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean ± SD) were obtained: left versus right hand 65.03% ± 8.52, left hand versus feet 68.19% ± 11.08, and right hand versus feet 65.05% ± 9.25. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70% but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects.

Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Abstract: Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship.

Flexibility and practicality graz brain-computer interface approach.

Abstract: "Graz brain-computer interface (BCI)" transforms changes in oscillatory electroencephalogram (EEG) activity into control signals for external devices and feedback. Steady-state evoked potentials (SSEPs) and event-related desynchronization (ERD) are employed to encode user messages. User-specific setup and training are important issues for robust and reliable classification. Furthermore, in order to implement small and thus affordable systems, focus is put on the minimization of the number of EEG sensors. The system also supports the self-paced operation mode, that is, users have on-demand access to the system at any time and can autonomously initiate communication. Flexibility, usability, and practicality are essential to increase user acceptance. Here, we illustrate the possibilities offered by now from EEG-based communication. Results of several studies with able-bodied and disabled individuals performed inside the laboratory and in real-world environments are presented; their characteristics are shown and open issues are mentioned. The applications include the control of neuroprostheses and spelling devices, the interaction with Virtual Reality, and the operation of off-the-shelf software such as Google Earth.

The Graz Brain-Computer Interface

Abstract: Brain-computer interface (BCI) research at the Graz University of Technology started with the classification of event-related desynchronization (ERD) [36, 38] of single-trial electroencephalographic (EEG) data during actual (overt) and imagined (covert) hand movement [9, 18, 40] At the beginning of our BCI research activities we had a cooperation with the Wadsworth Center in Albany, New York State, USA, with the common interest to control one-dimensional cursor movement on a monitor through mental activity [69] With such a cursor control it is in principle possible to select letters of the alphabet, create words and sentences and realize a thought-based spelling system for patients in a complete or incomplete “locked-in” state [68] At that time we already analyzed 64-channel EEG data from three patients who had accomplished a number of training sessions with the aim to search for optimal electrode positions and frequency components [38] Using the distinction sensitive learning vector quantizer (DSLVQ) [54] it was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control This was confirmed recently by BCI studies in untrained subjects [2, 58]

Implementation of Error Detection into the Graz-Brain-Computer Interface, the Interaction Error Potential

Abstract: A Brain-Computer Interface (BCI) represents the ultimate means of communication for people with severe paralyses or who are in a locked-in state. However, the usage of BCI is still severely limited in terms of accuracy and performance speed. One possible way to overcome these restrictions would be the detection of errors after incorrect events in the electroencephalogram (EEG). In this study 13 subjects participated in a first experiment to provide data for offline analysis of interaction error potentials (ErrPs) which were recorded after observation of falsely interpreted user-commands by an interface. These characteristic waveforms were later used to classify errors in online experiments combined with motor imagery (MI). Here, the detection of false movements could improve the accuracy significantly.

Non-invasive control of neuroprostheses for the upper extremity: Temporal coding of brain patterns

Abstract: Spinal cord injury (SCI) results in deficits of sensory, motor and autonomous functions, with tremendous consequences for the patients. The loss of motor functions, especially 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 with preserved voluntary shoulder and elbow function. In patients with lesions above the 5th vertebra, not only the voluntary movements of the elbow are restricted, but also the overall number of preserved movements available for control purposes decreases. A Brain-Computer Interface (BCI) offers a method to overcome this problem. This work gives an overview of the Graz BCI used for the control of grasp neuroprostheses as well as a new control method for combining grasp and elbow function is introduced.

Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand

Abstract: About 300,000 people in Europe alone suffer from a spinal cord injury (SCI), with 11,000 new injuries per year [20]. SCI is caused primarily by traffic and work accidents, and an increasing percentage of the total population also develops SCI from diseases like infections or tumors. About 70% of SCI cases occur in men. 40% are tetraplegic patients with paralyses not only of the lower extremities (and hence restrictions in standing and walking) but also of the upper extremities, which makes it difficult or impossible for them to grasp.