Gert Pfurtscheller

Event-Related EEG/MEG Synchronization and Desynchronization: Basic Principles

An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.

Optimal spatial filtering of single trial EEG during imagined hand movement

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.

Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz +/- 0.4 (mean +/- SD). While the desynchronized components were broad banded and centered at 10.9 Hz +/- 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz +/- 1.0. The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event-related synchronization (ERS) patterns were induced in at least one or two tasks. This implies that such EEG phenomena may be utilized in a multi-class brain-computer interface (BCI) operated simply by motor imagery.

Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest

Oscillations in the alpha and beta bands can display either an event-related blocking response or an event-related amplitude enhancement. The former is named event-related desynchronization (ERD) and the latter event-related synchronization (ERS). Examples of ERS are localized alpha enhancements in the awake state as well as sigma spindles in sleep and alpha or beta bursts in the comatose state. It was found that alpha band activity can be enhanced over the visual region during a motor task, or during a visual task over the sensorimotor region. This means ERD and ERS can be observed at nearly the same time; both form a spatiotemporal pattern, in which the localization of ERD characterizes cortical areas involved in task-relevant processing, and ERS marks cortical areas at rest or in an idling state.

Motor imagery activates primary sensorimotor area in humans

The spatiotemporal patterns of Rolandic mu and beta rhythms were studied during motor imagery with a dense array of EEG electrodes. The subjects were instructed to imagine movements of either the right or the left hand, corresponding to visual stimuli on a computer screen. It was found that unilateral motor imagery results in a short-lasting and localized EEG change over the primary sensorimotor area. The Rolandic rhythms displayed an event-related desynchronization (ERD) only over the contralateral hemisphere. In two of the three investigated subjects, an enhanced Rolandic rhythm was found over the ipsilateral side. The pattern of EEG desynchronization related to imagination of a movement was similar to the pattern during planning of a voluntary movement.

EEG-based discrimination between imagination of right and left hand movement

Three subjects were asked to imagine either right or left hand movement depending on a visual cue stimulus. The interval between two consecutive imagination tasks was > 10 s. Each subject imagined a total of 160 hand movements in each of 3-4 sessions (training) without feedback and 7-8 sessions with feedback. The EEG was recorded bipolarly from left and right central and parietal regions and was sampled at 128 Hz. In the feedback sessions, the EEG from both central channels was classified on-line with a neural network classifier, and the success of the discrimination between left and right movement imagination was given within 1.5 s by means of a visual feedback. For each subject, different frequency components in the alpha and beta band were found which provided best discrimination between left and right hand movement imagination. These frequency bands varied between 9 and 14 Hz and between 18 and 26 Hz. The accuracy of on-line classification was approximately 80% in all 3 subjects and did not improve with increasing number of sessions. By averaging over all training and over all feedback sessions, the EEG data revealed a significant desynchronisation (ERD) over the contralateral central area and synchronisation (ERS) over the ipsilateral side. The ERD/ERS patterns over all sessions displayed a relatively small intra-subject variability with slight differences between sessions with and without feedback.

Designing optimal spatial filters for single-trial EEG classification in a movement task

We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multichannel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the methods procedural and computational simplicity make it a particularly promising method for an EEG-based brain-computer interface.

Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement ☆

A method of accurate storage and on-line preprocessing of an EEG signal, preceding and following a trigger signal, elicited by button pressing, is described. The method was used to study the changes occurring in the power of the rhythmic activity within the alpha band in central areas, during voluntary, self-paced movement in 10 normal humans. A short-lasting decrease or phasic event-related desynchronization (ERD) of alpha power, representing mu activity, was observed in all 10 subjects. During the 2 sec period preceding movement the phasic ERD was mostly bilateral, but larger prior to right than to left thumb movement. At onset and during the first second of execution of movement, the phasic ERD was mostly bilateral but predominant in ipsilateral areas. Preceding or during movement, maximum ERD was observed in most cases in central-vertex regions.

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.

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 users 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.