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Objective: We investigated the influence of symmetry assumptions implicit in the derivation and the use of event-related lateralized potentials (ERLs), such as the lateralized readiness potential (LRP). We describe these assumptions and demonstrate several alternative computational methods.Methods: Using analytical methods and forward simulations, we computed the error in the ERL topography that results from deviations in symmetry between homologous brain areas. Based on analytical considerations we show that, for source analysis, the ERL derivation provides no benefits compared to a single subtraction of the two (left-lateralized and right-lateralized) conditions underlying the ERL.Results: Relative errors of 10% in the ERL topography are found if the location of an active region in one hemisphere differs by 10 mm from the symmetric location as compared to the other hemisphere A difference of 30° in orientation results in a relative error of the ERL of 40%. Differences in source strength between hemispheres result in an ERL error that is half the size of the relative strength difference.Conclusions: We estimate that, due to violations of the symmetry assumption underlying the ERL, errors in the ERL topography of 10–40% can be expected. Source analysis does not benefit from the ERL. In topographic mapping and source analysis, the double subtraction of the ERL should be approached with caution and the single subtraction of the ERPs of two lateralized conditions should be first analyzed whenever possible. We suggest that analyses based on the topography of the ERL should only be performed after the assumption of symmetry has been validated. [Copyright &y& Elsevier]

• An IFCN Workgroup supplies recommendations on EEG frequency and topographical analysis for research. • EEG recording, visualization, and extraction/interpretation best features are proposed. • Pros and cons for clinical research of those features are discussed in light of controversies. In 1999, the International Federation of Clinical Neurophysiology (IFCN) published "IFCN Guidelines for topographic and frequency analysis of EEGs and EPs" (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of "synchronization" features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of "connectivity" features based on frequency analysis (linear and nonlinear measures); (6) extraction of "topographic" features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising "synchronization" and "connectivity" features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Investigate the effectiveness and frequency dependence of central drive transmission via the α-motoneuron pool to the muscle. Methods: We describe a model for the simulation of α-motoneuron firing and the EMG signal as response to central drive input. The transfer in the frequency domain is investigated. Coherence between stochastical central input and EMG is also evaluated. Results: The transmission of central rhythmicities to the EMG signal relates to the spectral content of the latter. Coherence between central input to the α-motoneuron pool and the EMG signal is significant whereby the coupling strength hardly depends on the frequency in a range from 1 to 100Hz. Common central input to pairs of α-motoneurons strongly increases the coherence levels. The often-used rectification of the EMG signal introduces a clear frequency dependence. Conclusions: Oscillatory phenomena are strongly transmitted via the α-motoneuron pool. The motoneuron firing frequencies do play a role in the transmission gain, but do not influence the coherence levels. Rectification of the EMG signal enhances the transmission gain, but lowers coherence and introduces a strong frequency dependency. We think that it should be avoided. Significance: Our findings show that rhythmicities are translated into α-motoneuron activity without strong non-linearities. [ABSTRACT FROM AUTHOR]