Biomedical subjects

Bernhard Graimann

Publications and source records attributed to Bernhard Graimann.

10 recordsLinked to original sources

PubMed2006

Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients.

A number of electroencephalographic (EEG) studies report on motor event-related desynchronization and synchronization (ERD/ERS) in the beta band, i.e. a decrease and increase of spectral amplitudes of central beta rhythms in the range from 13 to 35 Hz. Following an ERD that occurs shortly before and during the movement, bursts of beta oscillations (beta ERS) appear within a 1-s interval after movement offset. Such a post-movement beta ERS has been reported after voluntary hand movements, passive movements, movement imagination, and also after movements induced by functional electrical stimulation. The present study compares ERD/ERS patterns in paraplegic patients (suffering from a complete spinal cord injury) and healthy subjects during attempted (active) and passive foot movements. The aim of this work is to address the question, whether patients do have the same focal beta ERD/ERS pattern during attempted foot movement as healthy subjects do. The results showed midcentral-focused beta ERD/ERS patterns during passive, active, and imagined foot movements in healthy subjects. This is in contrast to a diffuse and broad distributed ERD/ERS pattern during attempted foot movements in patients. Only one patient showed a similar ERD/ERS pattern. Furthermore, no significant ERD/ERS patterns during passive foot movement in the group of the paraplegics could be found.

Adolescent↗

PubMed2006

Quantification and visualization of event-related changes in oscillatory brain activity in the time-frequency domain.

In this chapter we review the traditional approach for ERD/ERS quantification and a more recent approach based on wavelet transform. In particular, we address the visualization of these phenomena and the validation of the results through statistical significance testing. Furthermore, we report on preprocessing using independent component analysis (ICA) and introduce a novel ERD/ERS maximization method.

Algorithms↗

PubMed2006

A comparison of common spatial patterns with complex band power features in a four-class BCI experiment.

We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future. A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results. As reference methods we used traditional band power features and the method of common spatial patterns. We consider also for the first time in the context of a four-class problem the issue of variability of the features over time and how much data is required to give good classification results. We do this in a practical way where training data precedes testing data in time.

Algorithms↗

PubMed2006

Online control of a brain-computer interface using phase synchronization.

Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence suggests that additional information is obtained by quantifying the relationship between signals of single electrodes, which might provide innovative features for future BCI systems. This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV). In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session.

Adolescent↗

PubMed2004

Phase relationships between different subdural electrode recordings in man.

Almost all brain-computer interfaces (BCIs) ignore information related to the phase coupling between electroencephalogram (EEG) or electrocorticogram (ECoG) recordings from different electrodes. This paper investigates whether additional information can be found when calculating the amount of synchronization between two electrode channels by using a phase locking measurement called the phase locking value (PLV). Special emphasis is put on the beta band (around 20 Hz) as well as the gamma band (high frequencies up to 95 Hz), which can only be used when subdural electrode recordings are available.

Action Potentials↗

PubMed2004

Phase coupling between different motor areas during tongue-movement imagery.

Motor imagery can be accompanied by an enhancement of brain oscillations (event-related synchronization, ERS) within specific frequency bands. To characterize the neuronal couplings involved during these prominent power changes, we have chosen a certain coupling measure that bears directly on the issue of transient cortical connections. Specifically, we applied for the first time the phase-locking value to investigate the phase coupling of sensorimotor rhythms in different motor areas during tongue-movement imagery. Most interesting, we showed that robust neuronal couplings within the alpha frequency range are established between the midcentral position and bilateral central electrode positions, overlying the supplementary motor area (SMA) and the right and left primary sensorimotor area, respectively. In contrast, no direct linkage was present between sensorimotor rhythms in both hemispheres. We suggest that the coupling results point at a separate interplay between neural networks within the SMA and lateralized networks in primary sensorimotor areas of each hemisphere during motor imagery.

Adult↗

PubMed2004

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

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.

Action Potentials↗

PubMed2004

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

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 sigma, the number of correctly spelled letters/min, up to sigma = 3.38 (average sigma = 1.99).

Algorithms↗

PubMed2004

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

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.

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↗