Data for: Stimulus transformation into motor action: dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjectshttps://doi.org/10.26165/JUELICH-DATA/T1PKNZRosjat, NilsWang, Bin A.Liu, LiqingFink, Gereon R.Daun, SilviaJülich DATA2020-12-092022-03-16T15:08:13Z<h1>Data description</h1> This dataset includes EEG recordings of 18 younger healthy subjects (18-35yrs) and 24 older healthy subjects (60+yrs) while they performed a visually cued finger tapping task. The task includes a visually-cued index finger tapping task and a vision-only control condition. <br><br> <h1>Preprocessing:</h1> The data is preprocessed in the following manner: The raw data were first bandpass filtered from 0.5 to 48 Hz to increase the signal-to-noise ratio and to avoid a potential of 50 Hz as an electric current artifact and then downsampled from 2500 Hz to 200 Hz. Next, the continuous raw EEG data were visually inspected for paroxysmal and muscular artifacts not related to eye blinks. Noisy portions of the signal were excluded from further analysis. All trials in the Visually-cued condition with incorrect responses were excluded, as well as trials with response times (RT) greater than 1s. The data is epoched (-1.5 to 2.5 s) centered around stimulus onset. After segmenting the continuous EEG data, the obtained epochs were corrected for artifacts. First, epochs were rejected if the amplitude over the entire epoch was larger than 100 μV or showed an abnormal drift that exceeded 75 μV. Next, a semi-automated procedure based on independent component analysis (ICA) was used to identify epochs contaminated by artifacts such as blinks, eye movements, muscle activity, and infrequent single-channel noise. The independent component decomposition was performed using the Infomax ICA algorithm implemented in EEGLAB. The ADJUST algorithm (Mognon et al., 2011) was then used to identify and reject components containing blink/oculomotor or other artifacts that were distinguishable from the rest of the brain activity. Noisy channels were detected automatically by EEGLAB and interpolated using spherical spline interpolation. Finally, the artifact-free trials were average-referenced and baseline-corrected.Medicine, Health and Life SciencesEEGAgingExternally triggerede movementsEnglishStimulus transformation into motor action: dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects Nils Rosjat, Bin A. Wang, Liqing Liu, Gereon R. Fink, Silvia Daun bioRxiv 2020.02.26.966325, doi, 10.1101/2020.02.26.966325, https://www.biorxiv.org/content/10.1101/2020.02.26.966325v3Rosjat, NilsRosjat, Nils2020-09-10https://figshare.com/articles/dataset/Data_from_Age-related_changes_in_oscillatory_power_affect_motor_action/5568271EEG recordingsCCBY