Showing papers by "Gert Pfurtscheller published in 2009"

Brain-Computer Interfaces: A Gentle Introduction

Abstract: Stardate 3012.4: The U.S.S. Enterprise has been diverted from its original course to meet its former captain Christopher Pike on Starbase 11. When Captain Jim Kirk and his crew arrive, they find out that Captain Pike has been severely crippled by a radiation accident. As a consequence of this accident Captain Pike is completely paralyzed and confined to a wheelchair controlled by his brain waves. He can only communicate through a light integrated into his wheelchair to signal the answers “yes” or “no”. Commodore Mendez, the commander of Starbase 11, describes the condition of Captain Pike as follows: “He is totally unable to move, Jim. His wheelchair is constructed to respond to his brain waves. He can turn it, move it forwards, backwards slightly. Through a flashing light he can say ‘yes’ or ‘no’. But that’s it, Jim. That is as much as the poor ever can do. His mind is as active as yours and mine, but it’s trapped in a useless vegetating body. He’s kept alive mechanically. A battery driven heart. …”

Neurofeedback Training for BCI Control

Abstract: Brain–computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2–4].

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.

Dimensionality reduction and channel selection of motor imagery electroencephalographic data

Abstract: The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centroparietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.

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.

Dynamics of Sensorimotor Oscillations in a Motor Task

Abstract: Many BCI systems rely on imagined movement. The brain activity associated with real or imagined movement produces reliable changes in the EEG. Therefore, many people can use BCI systems by imagining movements to convey information. The EEG has many regular rhythms. The most famous are the occipital alpha rhythm and the central mu and beta rhythms. People can desynchronize the alpha rhythm (that is, produce weaker alpha activity) by being alert, and can increase alpha activity by closing their eyes and relaxing. Sensory processing or motor behavior leads to EEG desynchronization or blocking of central beta and mu rhythms, as originally reported by Berger [1], Jasper and Andrew [2] and Jasper and Penfield [3]. This desynchronization reflects a decrease of oscillatory activity related to an internally or externally-paced event and is known as Event–Related Desynchronization (ERD, [4]). The opposite, namely the increase of rhythmic activity, was termed Event-Related Synchronization (ERS, [5]). ERD and ERS are characterized by fairly localized topography and frequency specificity [6]. Both phenomena can be studied through topographiuthc maps, time courses, and time-frequency representations (ERD maps, [7]).

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.

Design Method of Morphological Structural Function for Pattern Recognition of EEG Signals During Motor Imagery and Cognition

Abstract: Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen (such system is called BCI). Recently, we have proposed the detection method of Error Potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract features concerned with motor imagery and cognition simultaneously from the EEG signals. Morphological filter is composed of nonlinear operation between signal and structural function and this multi-resolution analysis can be constructed by repeating this filtering to signal while changing structural function. This method is a kind of discrete wavelet analysis with non-linear characteristics and is effective to extract specific shapes. The structural function which decides the filter characteristic is designed to obtain optimal separation based on mutual information algorithm or spectrum dividing algorithm.

Pattern recognition of EEG signals during motor imagery by using robust method

Abstract: Electroencephalograph (EEG) recordings during the right and the left hand motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by e.g., handicap users with amyotrophic lateral sclerosis (ALS). The conventional method purposes the recognition of the right hand and the left hand motor imagery. In this study, statistical pattern recognition method based on AR model is introduced to discriminate the EEG signals recorded during the right hand, the left hand and the right foot motor imagery. And the recognition of the hand motor imagery and the foot motor imagery also is carried out. Finally, the effectiveness of our method is confirmed through the experimental studies

Konzept zur Erfassung spontaner biologischer Oszillationen in der Anästhesie und Intensivmedizin

Abstract: Zur Erfassung und Beurteilung spontaner biologischer Oszillationen von Patienten im Bereich der Anästhesiologie und Intensivmedizin wird ein computerisierter multiparametrischer Meßplatz vorgestellt. Die Methodik bietet die Möglichkeit zur synchronen Aufzeichnung, Verarbeitung und Speicherung von Elektroenzephalogramm (EEG) , Atemfrequenz (AF) , Atemfrequenzvariabilität (AV) , Herzrate ( H R ) , Herzratenvariabilität (HRV), Mikrotremor bzw. Aktogramm (MT), Sauerstoffsättigung (SaO2), Temperatur (Tl=Raumtemp.; T2=axil. Temp.) und Blutdruck (BP). Offline können z.B. die Barorezeptorreflexsensitivität (aQ) und verschiedenste Spektralparameter (EEG, HRV, Atmung) bestimmt werden.

Feature extraction of EEG during motor imagery and cognition by using morphological MRA

Abstract: Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. Recently, we have proposed the detection method of Error Potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract features concerned with motor imagery and cognition simultaneously from EEG signals. Morphological filter is composed of nonlinear operation between signal and structural function. We propose some design methods of structural function that decide the filter characteristic of morphology. These algorithms are compared to DWT from the view point of filter characteristics. Consequently, effectiveness of our method is confirmed.

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.

Neue trends zur methodik der überwachung des intensivpatienten

Abstract: Zusammenfassung. Die multivariable Intensivüberwachung gewinnt zunehmend an Bedeutung. Am Beispiel von zwei komatösen Patienten (20jähriger Mann mit einem \"good recovery\" und 36-jährige Frau mit \"letalem outcome\" wird über den Einsatz eines neuen nichtinvasiven, computerunterstützten Langzeitmonitoringsystems berichtet. Schlüsselwörter: Multivariable Intens ivüberwachung, Koma, Hirntod, biologische Rhythmen.