Showing papers by "Gert Pfurtscheller published in 2004"

The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials

Abstract: Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.

An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate

Abstract: An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents an asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate /spl sigma/, the number of correctly spelled letters/min, up to /spl sigma/=3.38 (average /spl sigma/=1.99).

Conversion of EEG activity into cursor movement by a brain-computer interface (BCI)

Abstract: The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.

Continuous EEG classification during motor imagery-simulation of an asynchronous BCI

Abstract: Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.

Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis

Abstract: Highly accurate asynchronous detection of movement related patterns in individual electrocorticogram channels has been shown using detection based on either event-related potentials (ERPs) or event-related desynchronization and synchronization (ERD/ERS). A method using wavelet-packet features selected with a genetic algorithm was proposed to simultaneously detect ERP and ERD/ERS and was tested on data from seven subjects and four motor tasks. The proposed wavelet method performed better than previous methods with perfect detection for four subject/task combinations and hit percentages greater than 90% with false positive percentages less than 15% for at least one task for all seven subjects.

Walking through a virtual city by thought

Abstract: This paper gives a short overview of the feasibility of walking through a virtual city by using motor imagery. Therefore an electroencephalogram-based brain-computer interface (BCI) is combined with virtual reality technology. A BCI transforms bioelectrical brain signals, modulated by mental activity (e.g. imagination of foot or right hand movements), into a control signal. This signal is used to walk forward / backward or to remain stationary inside a virtual city. Results of the first experimental sessions are presented.

Navigation in Virtual Environments through Motor Imagery

Abstract: In this paper we report on the navigation in a virtual environment by the output signal of an EEG-based Brain-Computer Interface (BCI). Such a BCI transforms bioelectrical brain signals, modified by mental activity into a control signal. At this time only 1-D or 2-D BCI feedbacks are used. The graphical possibilities of virtual reality (VR) should help to improve BCI-feedback presentations and create new paradigms, with the intention to obtain a better control of the BCI. In this study the subjects had to imagine left or right hand movements and thereby exploring a virtual conference room. With a left hand motor imagery the subject turned in the room to the left and vice versa. Three trained subjects reached 77% to 100% classification accuracy in the course of the experimental sessions.

Lexical memory search during N400: Cortical couplings in auditory comprehension

Abstract: In this study, we applied partial-directed EEG-coherence analysis to assess regional changes in neuronal couplings and information transfer related to semantic processing. We tested the hypothesis whether (and which) processing differences between spoken words and pseudowords are reflected by changes in cortical networks within the time window of a specific event related potential (ERP) component, the N400. Fourteen native speaking German subjects performed a lexical decision paradigm, while being confronted sequentially with two-syllabic nouns and phonologically legal pseudowords. Using ERP analysis, we defined the time window of the N400 effect, known to reflect semantic processing, and, subsequently, we examined the coupling differences. Lexico-semantic memory search appears to be subserved by a network between temporal, parietal and frontal areas, particularly restricted to the left hemisphere.

Heart-Rate Variability and Event-Related ECG in Virtual Environments

Abstract: Beside standard questionnaires physiological measures can be used to describe the state of Presence in a virtual environment. A total of 21 participants explored a virtual bar in a CAVE like system. The experiment was divided into a baseline-, training- and experimental phase. During the experimental phase breaks in presence (BIPS) in form of whiteouts of the virtual environment scenario were induced. The heart rate variability and the event-related ECG were calculated from the acquired ECG data. The study showed that the heart rate variability can be used as parameter that reflects the physiological state of the participant. The event-related ECG showed the effect of the induced BIPS and of speaking avatars on the subjects.

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

Abstract: 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. Results: We found differences between patients and controls for ERS values at electrode positions C3 and Cz. At C3, the lower beta band ERS (14-20 Hz) in the RLS group was 101.2% compared with 27.5% in the control group (P <.05); in the upper beta band, (20-32 Hz) the findings were 97.8% and 29.0%, respectively, for the RLS and control groups (P <.01). At electrode Cz, no significant difference could be found in the lower beta band, but, for the upper beta band, patients showed significantly higher values than did the healthy control subjects (68.5% vs 25.6%, P <.05). Conclusions: We interpret these findings as a higher need for motor-cortical inhibition in RLS patients due to an increased level of excitation by motor-cortex activation and input from neighboring functionally interrelated cortical areas (hand and foot region). These results reveal new potentially important findings of the neurophysiologic and pathophysiologic origin of primary RLS.

Chapter 62 Brain-computer communication based on the dynamics of brain oscillations

Abstract: Publisher Summary A brain computer interface (BCI) is an assistive device that recognizes voluntary commands from the brain and triggers appropriate external responses, without requiring physical movement. The ultimate goal of such an interface is to provide effective communication without using the normal neuromuscular output pathways of the brain but by accepting commands directly encoded in neurophysiological signals. To be as effective as possible, an ideal BCI should provide multiple independently controllable channels, support high information-transfer rates, and allow the user to determine when a command is to be initiated. It should also require minimal conscious attention. This chapter provides a review of brain–computer communication based on motor imagery and the dynamics of brain oscillations. The concept of motor imagery as experimental strategy and the two different modes of operation of brain–computer interface are explained.

Navigating Virtual Reality by Thought: First Steps

Abstract: We have set up a brain-computer interface to be used as an input device to a highly immersive virtual reality Cave-like system. We have carried out two navigation experiments: three subjects were required to rotate in a virtual bar room by imagining left or right hand movement, and to walk along a single dimension in a virtual street by imagining foot or hand movement. In this paper we focus on the subjective user experience of navigating virtual reality “by thought”, and on the interrelations between BCI and presence.

Brain-Computer Interface - state of the art and future prospects

Abstract: A Brain-Computer Interface (BCI) is a communication system in which messages or commands that a user wishes to convey pass not through the brain's normal output pathways to the muscles but are instead extracted directly from brain signals. The basis for this is that mental activity (thought) can modify the bioelectrical brain activity and is therefore encoded in the recorded signals.

Feature extraction of EEG signals during right and left motor imagery

Abstract: In this paper, feature extraction of EEG signals during right and left motor imagery is tried. As the extraction method, directed information analysis, pattern recognition based on AR model, and mixture probability algorithm based on AR model is used. The learning and feature extraction method in pattern recognition are discussed through the experimental studies.

EEG signal analysis based on quasi-AR model: application to EEG signals during right and left motor imagery

Abstract: In this paper, quasi-AR model is introduced in order to analyze EEG signals. And it is discussed that feature extraction method by using linear structure of quasi-AR model, and statistical pattern recognition method. Applying to EEG signals during right and left motor imagery, the validity of quasi-AR model is confirmed.

EEG Signal Analysis based on Quasi-AR Model

Abstract: In this paper, quasi-AR model is introduced in order to analyze EEG signals. And it is discussed that feature extraction method by using linear structure of quasi-AR model, and statistical pattern recognition method. Applying to EEG signals during right and left motor imagery, the validity of quasi-AR model is confirmed.