Biomedical subjects

Alois Schlögl

Publications and source records attributed to Alois Schlögl.

10 recordsLinked to original sources

PubMed2006

Study of discriminant analysis applied to motor imagery bipolar data.

We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.

Brain↗

PubMed2006

Analyzing event-related EEG data with multivariate autoregressive parameters.

Methods of spatio-temporal analysis provide important tools for characterizing several dynamic aspects of brain oscillations that are reflected in the human scalp-detected electroencephalogram (EEG). The search to identify the dynamic connectivity of brain signals within different frequency bands, in order to uncover the transient cooperation between different brain sites, converges at the potential of multivariate autoregressive (MVAR) models and their derived parameters. In fact, MVAR parameters provide a whole battery of so-called coupling measures including classic coherence (COH), partial coherence (pCOH), imaginary part of coherence (iCOH), partial-directed coherence (PDC), directed transfer function (DTF), and full frequency directed transfer function (ffDTF). All of these approaches have been developed to quantify the degree of coupling between different EEG recording positions, with the specific aim to characterize the functional interaction between neural populations within the cortex. This work addresses the application of MVAR models to event-related brain processes, including different statistical approaches, and reviews most relevant findings in the expanding field of coupling analysis. Finally, we present several examples of coupling patterns associated with certain types of movement imagery.

Algorithms↗

PubMed2006

BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.

Algorithms↗

PubMed2006

The BCI competition. III: Validating alternative approaches to actual BCI problems.

A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

Algorithms↗

PubMed2005

Characterization of four-class motor imagery EEG data for the BCI-competition 2005.

To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.

Algorithms↗

PubMed2005

Hemodynamic and autonomic changes induced by Ironman: prediction of competition time by blood pressure variability.

We hypothesized that the extreme endurance exercise of an Ironman competition would lead to long-standing hemodynamic and autonomic changes. We investigated also the possibility of predicting competition performance from baseline hemodynamic and autonomic parameters. We have investigated 27 male athletes before competition, 1 h after, and then for the following week after the competition. The Task Force monitor was used to measure beat-to-beat hemodynamic and autonomic parameters during supine rest and active standing. Heart rate (P < 0.001) was increased, and stroke index (P = 0.011), systolic blood pressure (P = 0.004), diastolic blood pressure (P < 0.001), total peripheral resistance index (P < 0.001), and baroreceptor reflex sensitivity (P < 0.001) were decreased after the competition. The 0.05- to 0.17-Hz band of heart rate and blood pressure variability was increased (P < 0.001 and P < 0.001, respectively), the 0.17- to 0.40-Hz band of heart rate interval variability was decreased after the competition (P < 0.001). All parameters returned to baseline values 3 days after the competition. After the competition, the autonomic response to orthostasis was significantly impaired. The 0.05- to 0.17-Hz band of diastolic blood pressure variability before competition and weekly net exercise training, but not the other hemodynamic and autonomic parameters, were related to competition time in multivariate regression analysis (multiple r = 0.70, P < 0.001). The marked hemodynamic and autonomic changes after an ultraendurance race, which are compatible with myocardial depression in the face of sympathetic activation and reduction of afterload, return to baseline after only 1-3 days. Because the 0.05- to 0.17-Hz band of diastolic blood pressure variability contributes to the prediction of competition time, the analysis of blood pressure variability in the frequency domain deserves further study for the prediction of endurance capacity.

Adaptation, Physiological↗

PubMed2005

Semantic memory retrieval: cortical couplings in object recognition in the N400 window.

To characterize the regional changes in neuronal couplings and information transfer related to semantic aspects of object recognition in humans we used partial-directed EEG-coherence analysis (PDC). We examined the differences of processing recognizable and unrecognizable pictures as reflected by changes in cortical networks within the time-window of a determined event-related potential (ERP) component, namely the N400. Fourteen participants performed an image recognition task, while sequentially confronted with pictures of recognizable and unrecognizable objects. The time-window of N400 as indicative of object semantics was defined from the ERP. Differences of PDC in the beta-band between these tasks were represented topographically as patterns of electrical couplings, possibly indicating changing degrees of functional cooperation between brain areas. Successful memory retrieval of picture meaning appears to be supported by networks comprising left temporal and parietal regions and bilateral frontal brain areas.

Adult↗

PubMed2004

Lexical memory search during N400: cortical couplings in auditory comprehension.

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.

Acoustic Stimulation↗

PubMed2004

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

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.

Adult↗

PubMed2003

Detection of movement-related desynchronization patterns in ongoing single-channel electrocorticogram.

Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.

Algorithms↗