Pre-processing wasconducted with the EEGLAB (Delorme and Makeig, 2004 (link))and ERP LAB (Lopez-Calderon and Luck, 2014 (link))toolboxes for MATLAB (MathWorks, Natick). EEG data were merged,re -referenced to the average of all electrodes, and filtered (0.1–30 Hz). Bad channels were interpolated, independent components analysis was used to remove activity reflectingblinks, HEOG, and EKG, and the cleaned data were time -locked to word onsets and segmented (−200 to 2000 ms). The pre-stimulus interval was used for baseline correction, and segments where any raw value or the maximum-minimum voltage difference (200 ms intervals, 100 ms sliding window) exceeded 100 μV were rejected. We used a priori criteria of > 18 bad channels or more than 50% of trials rejected (Luck, 2014 ) to exclude excessively noisy datasets (10 controls, 2 MDD). The mean number of clean segments in each bin defined by Group x Cuex Taskranged from 21–28 for source hits. Guesses were excluded and there were too few clean segments for analyzingmisses . Thus, the analysis was focused on correct responses, a commonapproach in this literature (Bergström et al., 2013 (link); Dobbins and Wagner, 2005 (link); Han et al., 2012 (link); Simons et al., 2005a (link)).
Erplab
Manufactured by MathWorks
Sourced in United States
ERPLAB is a software toolbox for MATLAB that provides a comprehensive set of tools for the analysis of event-related potentials (ERPs) - a widely used neurophysiological technique for studying brain function. ERPLAB offers a range of data processing, visualization, and analysis capabilities to researchers working with ERP data.
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8 protocols using erplab
1
EEG Pre-processing and Analysis Protocol
2
EEG Data Preprocessing and Artifact Removal
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Electroencephalography data were pre-processed and analyzed using EEGLAB (Delorme and Makeig, 2004 (link)) and ERPLAB (Lopez-Calderon and Luck, 2014 (link)) for MATLAB® (Mathworks). Continuously recorded data, re-referenced to average mastoids at import, was filtered to 256 Hz, for subsequent downsampling at 512 Hz, and high-pass filtered at 0.16 Hz to remove slow drifts. Channels whose amplitude exceeded 150 μV for more than 20% of the recording duration or whose power spectral density was 5 dB larger than the average power over all channels, were automatically labeled as bad. Channels marked as bad were visually inspected and confirmed for rejection. To further remove remaining artifacts, the EEG was filter to 0.16 and 100 Hz and decomposed by applying ICA to good channels only. ICs components with a 90% chance of being an artifact (Muscle, Eye, Heart, Line Noise, and Channel Noise) were automatically identified and removed using ICLabel1 (Pion-Tonachini et al., 2019 (link)) and eyeCatch2 (Bigdely-Shamlo et al., 2013 (link)). The signal was then reconstructed without the artefactual components and channels marked as bad were interpolated.
3
Cortical Dynamics in Postural Maintenance
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To understand cortical dynamics associated with postural maintenance, the same postural experiment was repeated while high- density EEG data were recorded. Due to physical and system restrictions, the EEG version of the experiment had to be conducted separately from the CoP experiment in a separate building on the University of Nevada, Reno campus.
Participants performed the same three postural tasks while EEG data were continuously recorded from a 128 channel BioSemi Active 2 system (BioSemi, Amsterdam, The Netherlands). Note that this was separate from the CoP collection and thus CoP data from force plate were not recorded simultaneously with EEG data. In addition to the standard 10–20 electrode locations, this system included intermediate positions. Default electrode labels were renamed to approximate the more conventional 10–20 system (see supplementary Figure S1 in [35 ]). Four additional channels recorded electrooculography signals, two channels on the lateral sides of each eye to detect horizontal movement and two channels above and below the right eye to detect vertical movement (i.e., blinks). EEG was sampled at a rate of 512 Hz and processed offline using EEGLAB (v.14_0_0b) and ERPLAB (v.6.1.3) with MATLAB R2013b (MathWorks, Natick, MA, United States).
Participants performed the same three postural tasks while EEG data were continuously recorded from a 128 channel BioSemi Active 2 system (BioSemi, Amsterdam, The Netherlands). Note that this was separate from the CoP collection and thus CoP data from force plate were not recorded simultaneously with EEG data. In addition to the standard 10–20 electrode locations, this system included intermediate positions. Default electrode labels were renamed to approximate the more conventional 10–20 system (see supplementary Figure S1 in [35 ]). Four additional channels recorded electrooculography signals, two channels on the lateral sides of each eye to detect horizontal movement and two channels above and below the right eye to detect vertical movement (i.e., blinks). EEG was sampled at a rate of 512 Hz and processed offline using EEGLAB (v.14_0_0b) and ERPLAB (v.6.1.3) with MATLAB R2013b (MathWorks, Natick, MA, United States).
4
EEG Data Preprocessing for Cognitive Study
5
Electrophysiological Data Collection and Processing
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Electrophysiological data were recorded from 64 Ag/AgCl electrodes according to the extended 10–20 convention and were referenced to Cz at a rate of 1 kHz. Impedances were kept < 5 k Ω and the electroencephalogram (EEG) activity was filtered online with a band-pass philtre between 0.05 and 200 Hz, and offline with a low-pass zero-phase shift digital philtre which was set at 25 Hz. The data were then pre-processed using MATLAB (R2014a, The Mathworks, Inc.), and the EEGLAB (Delorme and Makeig, 2004 (link)) and ERPLAB (Lopez-Calderon and Luck, 2014 (link)) toolboxes. The continuous EEG data was visually inspected, and excessive muscular artefacts were manually removed. Epochs ranging from −100 to 1000 ms from the onset of the target word were extracted from the EEG recordings, and an independent component analysis (ICA; e.g., Makeig and Onton, 2011 (link)) was performed to identify and extract remaining muscular and ocular artefacts. A maximum of five independent components were removed per participant. Epochs with activity exceeding ± 200 μV at any electrode site were automatically discarded. There was a minimum of 24 epochs per condition for every participant. Baseline correction was performed in reference to 100 ms of pre-stimulus activity, and individual averages were digitally re-referenced to the global average reference.
6
EEG Processing and Analysis Protocol
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EEG data were recorded on a 128-channel eego system (ANT Neuro, Enschede, The Netherlands) with a 1000-Hz sampling rate. CPz was used as the online reference and the ground electrode was placed close to the left mastoid. Electrode impedance was kept under 20kΩ during the experiment.
Preprocessing was conducted with EEGLAB (Delorme & Makeig, 2004 (link)) and ERPLAB (Lopez-Calderon & Luck, 2014) (link) in MATLAB (Mathworks, Natick, MA). Data were re-referenced off-line to the average reference. Band-pass filtering (0.01-30Hz) was applied to the continuous EEG data, which were then divided into 1000-ms epochs starting at 200ms before and ending at 800ms after the presentation of the object. Since the signals near the end of the stimulus presentation was not of interest, the last 200ms of stimuli presentation (i.e., 800-1000ms) was not used for artifact rejection to avoid rejecting epochs due to artifacts during this time window. Incorrect trials were also excluded from further analyses. Epochs with ocular artifacts were removed by visual inspection and by the moving-window peak-to-peak function in ERPLAB on VEOG, HEOG and the channels selected in the decoding analysis with a threshold of 100 μV, a window size of 200ms and a step size of 50ms. On average, 4.57% and 6.24% of the trials were rejected due to incorrect responses and artifacts respectively.
Preprocessing was conducted with EEGLAB (Delorme & Makeig, 2004 (link)) and ERPLAB (Lopez-Calderon & Luck, 2014) (link) in MATLAB (Mathworks, Natick, MA). Data were re-referenced off-line to the average reference. Band-pass filtering (0.01-30Hz) was applied to the continuous EEG data, which were then divided into 1000-ms epochs starting at 200ms before and ending at 800ms after the presentation of the object. Since the signals near the end of the stimulus presentation was not of interest, the last 200ms of stimuli presentation (i.e., 800-1000ms) was not used for artifact rejection to avoid rejecting epochs due to artifacts during this time window. Incorrect trials were also excluded from further analyses. Epochs with ocular artifacts were removed by visual inspection and by the moving-window peak-to-peak function in ERPLAB on VEOG, HEOG and the channels selected in the decoding analysis with a threshold of 100 μV, a window size of 200ms and a step size of 50ms. On average, 4.57% and 6.24% of the trials were rejected due to incorrect responses and artifacts respectively.
7
EEG Processing and Analysis Protocol
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EEG data were recorded on a 128-channel eego system (ANT Neuro, Enschede, The Netherlands) with a 1000-Hz sampling rate. CPz was used as the online reference and the ground electrode was placed close to the left mastoid. Electrode impedance was kept under 20kΩ during the experiment.
Preprocessing was conducted with EEGLAB (Delorme & Makeig, 2004 (link)) and ERPLAB (Lopez-Calderon & Luck, 2014) (link) in MATLAB (Mathworks, Natick, MA). Data were re-referenced off-line to the average reference. Band-pass filtering (0.01-30Hz) was applied to the continuous EEG data, which were then divided into 1000-ms epochs starting at 200ms before and ending at 800ms after the presentation of the object. Since the signals near the end of the stimulus presentation was not of interest, the last 200ms of stimuli presentation (i.e., 800-1000ms) was not used for artifact rejection to avoid rejecting epochs due to artifacts during this time window. Incorrect trials were also excluded from further analyses. Epochs with ocular artifacts were removed by visual inspection and by the moving-window peak-to-peak function in ERPLAB on VEOG, HEOG and the channels selected in the decoding analysis with a threshold of 100 μV, a window size of 200ms and a step size of 50ms. On average, 4.57% and 6.24% of the trials were rejected due to incorrect responses and artifacts respectively.
Preprocessing was conducted with EEGLAB (Delorme & Makeig, 2004 (link)) and ERPLAB (Lopez-Calderon & Luck, 2014) (link) in MATLAB (Mathworks, Natick, MA). Data were re-referenced off-line to the average reference. Band-pass filtering (0.01-30Hz) was applied to the continuous EEG data, which were then divided into 1000-ms epochs starting at 200ms before and ending at 800ms after the presentation of the object. Since the signals near the end of the stimulus presentation was not of interest, the last 200ms of stimuli presentation (i.e., 800-1000ms) was not used for artifact rejection to avoid rejecting epochs due to artifacts during this time window. Incorrect trials were also excluded from further analyses. Epochs with ocular artifacts were removed by visual inspection and by the moving-window peak-to-peak function in ERPLAB on VEOG, HEOG and the channels selected in the decoding analysis with a threshold of 100 μV, a window size of 200ms and a step size of 50ms. On average, 4.57% and 6.24% of the trials were rejected due to incorrect responses and artifacts respectively.
8
EEG Data Preprocessing and Artifact Removal
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EEG data were pre-processed and analysed offline using EEGLAB 19 , ERPLAB 20 and customized MATLAB scripts (Mathworks, Inc., MA, USA). EEG data was down-sampled to 250 Hz and subsequently band-pass filtered between 0.5 and 30 Hz with a zero phase-shift IIR Butterworth filter (24 dB/Oct). Noisy channels were identified and removed using automated procedures. To identify and remove ocular movements and blink artefacts from the EEG data, an independent component analysis (ICA) implemented within EEGLAB was used. The components were visually inspected and those containing ocular movements or blink artifacts were removed. The previously removed channels were then interpolated back into the dataset and finally, the EEG data was rereferenced against the grand average of all scalp electrodes.
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