Summary:The phenomena of event-related desynchronization (ERD) and synchronization (ERS) reflect the dynamics of neural networks and can be observed on different scalp locations at the same moment of time. Whereas on one cortical area a focal 10-Hz ERD can be found, other areas can display a 10-Hz ERS. This phenomenon is called focal ERD/surround ERS and is interpreted as a correlate of an activated cortical area (ERD) and simultaneously deactivated or inhibited other areas. The induced oscillations (ERS) are dominant in the 10-to 13-Hz band and very likely mediated by thalamic gating. Key Words: Alpha band rhythms-Cortical inhibition-Thalamic gating-Eventrelated desynchronization (ERD)-Event-related synchronization (ERS). EVENT-RELATED EEG (DE) SYNCHRONIZATIONOne characteristic feature of the brain is its ability to generate rhythmic potentials or oscillatory activity. This fact was discovered as early as 1949 by Jasper and Penfield (1), who also discussed the relation between alpha and beta rhythms and their functioning in relation to underlying neuronal networks. The frequency of brain oscillations depends on both membrane properties of single neurons and the organization and interconnectivity of networks to which they belong (2). Such a network can either comprise a large number of neurons controlled by thalamocortical feedback loops or only a small number of neurons interconnected by intracortical feedback loops. Coherent activity in large neuronal pools can result in high-amplitude, low-frequency oscillations (e.g., alpha band rhythms), whereas synchrony in localized neuronal pools can be the source of gamma oscillations (3).A neural network can display different states of synchrony whereby feedback loops can shape their dynamics and create oscillations in different frequency bands. The dynamic of such a network can result in phasic changes in the synchrony of cell populations due to externally or internally paced events and lead to characteristic EEG patterns. Two types of such patterns are observed, the event-related desynchronization or ERD in the form of an amplitude attenuation and the event-related synchronization or ERS in the form of an enhancement of specific frequency com-

Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. We present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.

Multi-channel electroencephalography recordings have shown that a visual cue, indicating right hand, left hand or foot motor imagery, can induce a short-lived brain state in the order of about 500 ms. In the present study, 10 able-bodied subjects without any motor imagery experience (naive subjects) were asked to imagine the indicated limb movement for some seconds. Common spatial filtering and linear single-trial classification was applied to discriminate between two conditions (two brain states: right hand vs. left hand, left hand vs. foot and right hand vs. foot). The corresponding classification accuracies (mean +/- SD) were 80.0 +/- 10.6%, 83.3 +/- 10.2% and 83.6 +/- 8.8%, respectively. Inspection of central mu and beta rhythms revealed a short-lasting somatotopically specific event-related desynchronization (ERD) in the upper mu and/or beta bands starting approximately 300 ms after the cue onset and lasting for less than 1 s.

Non-linear time sequence analysis has been performed on infant sleep measurement data in order to obtain more information about the respiratory processes. As a first step, respiration data during REM sleep were analysed with methods from non-linear dynamics, especially, the correlation integral and the slope of its log-log plot, representing the correlation dimension. Before calculation of the correlation integral, a special kind of filtering has to be applied to the data. This filtering algorithm is a state space and singular value decomposition-based noise reduction method, and it is used to separate the noise and signal subspaces. The dynamics of a signal (in our case data from the respiratory process) and its degrees of freedom can be characterised by the correlation integral and by the correlation dimension, respectively. The main result of this study is that the highly irregular-looking breathing patterns during REM sleep could be described by a deterministic system, and finally the physiological significance of this finding is discussed.

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