Offline data treatment was performed by means of EEGLAB software99 (link), ASA software (ANT Neuro system, The Netherlands) and in-house MATLAB-based tools. Data were resampled to 512 Hz, low pass (200 Hz) and high-pass (0.1 Hz) filtered, channels with abnormally high amplitude were interpolated, data were re-referenced to REST100 (link), visually inspected and portions of data presenting abnormally high amplitude were manually rejected. Artefactual components from eye movement (blinks and horizontal movement) were identified and rejected using the ICALabel plugin of the EEGLAB toolbox.
ERPs were calculated by averaging epochs extracted from − 1000 to 4000 ms of the stimulus event (i.e. memory cue: the appearance of the cue word) in the T and the NT conditions without baseline correction101 (link)–104 (link). After artifact rejection, we obtained a total of 4158 and 4259 epochs for the T and NT conditions respectively.
The significance in the ERPs (and their topographies) between conditions at the population level was calculated in EEGlab by permutation statistics (p < 0.05), corrected for multiple comparisons (ERPs from 64 electrodes) by using the false discovery rate (FDR) method.
ERPs were calculated by averaging epochs extracted from − 1000 to 4000 ms of the stimulus event (i.e. memory cue: the appearance of the cue word) in the T and the NT conditions without baseline correction101 (link)–104 (link). After artifact rejection, we obtained a total of 4158 and 4259 epochs for the T and NT conditions respectively.
The significance in the ERPs (and their topographies) between conditions at the population level was calculated in EEGlab by permutation statistics (p < 0.05), corrected for multiple comparisons (ERPs from 64 electrodes) by using the false discovery rate (FDR) method.
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