This storage contains the openly accessible EEG data and Matlab analysis scripts from [Benwell et al. (2019, NeuroImage)]. The data in Benwell et al. (2019, NeuroImage) were combined from two previous experiments, published as [Benwell et al. (2018, European Journal of Neuroscience)] and [Benwell et al. (2017, eNeuro)]. Hence, two folders can be found below containing the respective datasets. The single-participant datasets in each folder have been pre-processed as follows: eNeuro 2017: Continuous data were filtered for power line noise using a notch filter centred at 50Hz. Additional low (85 Hz) and high-pass (0.1 Hz) filters were applied using a zero-phase second order Butterworth filter. ICA was applied to identify and remove eye blinks and muscle artifacts. The data were segmented into epochs of 5 s starting -2.5 sec before stimulus onset and down sampled to 250 Hz. All epochs were then visually inspected and removed if contaminated by residual eye movements, blinks, strong muscle activity or excessive noise. Note that the behavioural data from the eNeuro paper are also available for each participant. Each single-subject file contains single-trial accuracy, PAS Ratings and trial types (i.e. contrast presented). See the 'trigger_info' Matlab cell structure for more information. Note that values of '9' in the accuracy vector indicate that this was a catch trial (i.e. no contrast) with no correct response. EJN 2018: Continuous data were filtered for power line noise using a notch filter centred at 50Hz. Additional low (100 Hz) and high-pass (0.1 Hz) filters were applied using a zero-phase second-order Butterworth filter. The data were then divided into epochs spanning -2.5:1.5 sec relative to stimulus onset on each trial. Subsequently, excessively noisy electrodes were removed without interpolation, the data were re-referenced to the average reference (excluding ocular channels) and trials with abnormal activity were rejected using a semi-automated artefact detection procedure. An independent component analysis (ICA) was then run using the runica EEGLAB function and components corresponding to blinks, eye movements and muscle artefacts were removed. Missing channels were then interpolated using a spherical spline method. See the respective publications for full details. The analysis scripts 'eNeuroPostProcess' and 'EJNPostProcess' perform the subsequent signal processing steps performed on each dataset respectively for Benwell et al. (2019, NeuroImage). The remaining scripts ('Figure1.m','Figure2.m'....) reproduce each figure in the manuscript (and the corresponding analyses). Any problems? Please contact firstname.lastname@example.org : https://pubmed.ncbi.nlm.nih.gov/30844505/ : https://www.ncbi.nlm.nih.gov/pubmed/28887893 : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732016/
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