Cortical oscillatory activity assessed by combined EEG and MEG recordings and High-Resolution ERD methods
Event-related desynchronization is a phenomenon of the awake brain and was quantified for the first time by Pfurtscheller and Aranibar [1]. ERD of alpha band rhythms can be seen äs a correlate of activated cortical neurons and was reported during sensory, motor and cognitive processing. In evoked potential/field studies the responses of the brain activity with respect to the Stimuli are phase locked to the event. In order to improve the signal-tonoise ratio (SNR) trials of EEG/MEG recordings are averaged with respect to the event. Source localization procedures or cortical imaging methods are then applied to the averaged potentials/fields. However, in event-related desynchroni-zation studies the response of the brain (i.e. the induced rhythmic activity) to the Stimuli is not phase locked to the event. Therefore, simple amplitude a veraging of trials with respect to the event can not be done properly to improve the SNR. To overcome this problem the non-stationary EventRelated Desynchronization analysis (ERD) was introduced [1]. The outcome of the ERD analysis resembles in some way an averaged evoked response signal with a temporal profile depending on the mean power ratio between the EEG in the actual measurement interval and a reference interval. It is assumed that most of the ongoing EEG observed on the scalp (by EEG recordings) or outside the head (by MEG recordings) is due to superficially located neo-cortical sources [2],[3]. Dipole layers can describe the sources. Based on fmdings by [4] mainly radially orientated dipole-layers (with respect to the cortical surface) contribute to the observed ERD/ERS. Combined EEG and MEG recordings have (at least theoretically) higher Information content for identifying brain source activity than single EEG and MEG recordings [5]. In order to investigate the usefulness of simultaneous EEG and MEG recordings the topographical differences in EEG and/or MEG maps based on simple self-paced finger movements are investigated. High-resolution ERD methods based on a distributed cortical source space [6] combining biomagnetic and bioelectric data are applied to the acquired data sets. Method