Event Abstract Back to Event Power changes due to visuo-motor task in scalp EEG and MEG source signals Silvia Erla1, 2*, Christos Papadelis2, Christoph Braun2, Luca Faes1 and Giandomenico Nollo1 1 Nicolaus Copernicus University, Athena project team, Poland 2 University of trento, Functional Neuroimaging Lab, CIMeC, Italy Aim: This work aims to quantify the main oscillatory components of the brain electromagnetic signals, as well as their possible task-induced changes by analyzing the power spectrum of scalp EEG signals and MEG virtual sensors. Methods: MEG and EEG signals were recorded from ten healthy subjects at rest and during performing a visuo-motor compensation task. The analysis was performed on the scalp EEG recordings (33 channels, 10-20 SI), and on 15 virtual sensors estimated from the MEG signals by using Synthetic Aperture Magnetometry (SAM) as implemented in the CTF software (1). Parametric autoregressive spectral decomposition (2) was applied to the two datasets, estimating the alpha, beta and gamma power for each subject, channel and condition. Three brain regions were considered (visual, motor and somatosensory cortex) and the average power was estimated from all electrodes belonging to each region. Three-way ANOVA was performed to test possible differences on power: (i) between the two conditions (rest, task); (ii) among cortex positions (occipital, motor, somatosensory); and (iii) between the two brain hemispheres. When ANOVA yielded a significant p value, post-hoc multiple testing was performed to assess differences between pairs of power distributions (Student t-test for paired data with Holm's correction). Results: In all considered regions, the power contents of EEG and MEG virtual sensors signals decreased during performing the visuo-motor task. Furthermore, during task, power spectra were higher in motor cortex than in occipital and somoatosensory related signals. Finally, alpha beta and gamma powers were significantly different between the two hemispheres. These results were consistent for both scalp EEG and MEG virtual sensors. Conclusion: These findings showed the ability of the considered spectral estimator to reveal the neuronal network activation during task performance engaging somatosensory, visual and motor regions. The agreement between the power spectra changes in scalp EEG and MEG source recording modalities substantiates the role of cortical signals analysis in neurosciences research encouraging a more detailed examination of the advantages of source signals estimation compared to scalp signals power analysis.