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WeBrain Pipeline List

  1. WB_EEG_REST is a tool of Reference Electrode Standardization Technique (REST) in WeBrain. REST is a re-reference technique, a software method for translating multichannel spontaneous EEG or event-related potentials with reference at any a physical point on brain/body surface or the post-processed data referenced at average or linked ears etc. to a new dataset with reference at Infinity where the potential is zero/constant (Yao, 2001; Yao et al., 2005). More details about REST toolbox can also be seen in the paper (Dong et al., 2017).
  2. WB_EEG_Mark is a tool to automatically mark bad block/good quality EEG data based on thresholding z-scores/global field power (z-scored standard deviation of the signal at all electrodes is calculated). It is recommended before calculating EEG indices (e.g. power, networks) or ERPs. Steps of marking EEG data consist of: [1] Filtering all EEG data (Passband filtering and Notch filter). [2] Z-transforming the EEG data/calculating global field power. [3] Averaging z-values/using global field power over channels/electrodes allows evidence for an artifact to accumulate and averaging it over channels. [4] Threshold the accumulated z-score/global field power for each epoch/window.
  3. WB_EEG_runICA is a tool to run ICA on EEG data based on EEGLAB function - runica(). It perform Independent Component Analysis (ICA) decomposition of input data using the logistic infomax ICA algorithm of Bell & Sejnowski (1995) with the natural gradient feature of Amari, Cichocki & Yang, or optionally the extended-ICA algorithm of Lee, Girolami & Sejnowski, with optional PCA dimension reduction. Annealing based on weight changes is used to automate the separation process. ICA is usually used to remove artifact (e.g. eye blink) or extract features (e.g. ERP) from EEG data.
  4. WB_EEG_QA is a stable tool to realize quality assessment (QA) of a continuous EEG raw data (e.g, resting-state EEG data). The bad data in small windows of each channel could be detected by kinds of 4 methods, and a number of indices related to the data quality will be calculated. Meanwhile, the overall data quality rating will be also provided, including levels of A, B, C, D (corresponding to perfect, good, poor, bad).
  5. WB_EEG_prepro is a specific and stable tool to perform standardized preprocessing of continuous EEG raw data to remove a kind of artifacts (e.g. resting state EEG data), and obtain clean EEG data with REST reference. Preprocessing EEG raw data consists of: [1] Quality assessment of EEG raw data first. Noting that quality assessment (QA) do NOT change the EEG raw data. If the overall data quality (ODQ) exceed a threshold (default is 80), then the preprocessing could be continue; [2] Passband and notch filtering, if applicable; [3] Artifact removal: EOG regression; [4] Artifact removal: residual artifact removal; [5] Bad channel interpolation and re-referencing to REST; [6] Quality assessment of preprocessed EEG data after artifact removal; [7] Marking residual bad block with unusually high or low amplitude using z-scored STD across channels, and then clean EEG data with REST reference are obtained finally.
  6. WB_EEG_CalcPower is a tool to calculate power indices using time-frequency analysis of EEGALB (using function timefreq()). Calculating power indices consists of: [1] Specific event data can be extracted according to the input ‘eventlabel’. [2] Specific event EEG signals will be divided into small epochs. [3] EEG data of each epoch (default is 5s epoch) was subjected to time-frequency analysis with Fast-Fourier Transform (FFT) to obtain the absolute EEG band power at each electrode in the specific bands.
  7. WB_EEG_CalcNetwork is a basic tool to calculate EEG network between EEG channels at scalp level. Calculating EEG network consists of: [1] Specific event data can be extracted according to the input ‘eventlabel’. [2] Specific EEG signals will be divided into small epochs. [3] EEG data of each epoch (default is 5-s epoch) was subjected to calculate correlation/coherence/PSI/PLV to obtain the EEG network across electrodes in the specific bands
  8. WB_EEG_CalcERP is a tool to create averaged event related potential (ERP) for each EEG channel at scalp level. Calculating ERP consists of: [1] Filter data using Hamming windowed sinc FIR filter. [2] Extract epochs (default is [-0.2 0.8] sec) and baseline correction ( [-0.2, 0] sec)[3] Artifact rejection in epoched data using simple voltage threshold. Three criterions including amplitude, gradient and max-min criterions were used to reject artifact trials. [4] ERP will be obtained from averaged clean epochs (default is [-0.2 0.8] sec).
  9. WB_EEG_CalcNetMeasures is a tool to calculate network measures based on graph theory using BCT toolbox. MATLAB .mat file will be imported as EEG_results structure using WeBrain tool ‘WB_EEG_CalcNetwork’. EEG_reuslts should contain connection matrix M (EEG_results.M) and EEG result type (EEG_results.type) at least. First two dimensions of EEG_results.M should be channels/nodes × channels/nodes, and EEG_results.type should be ‘network’. More details of network measures can be seen in relative paper (Rubinov & Sporns, 2010) and BCT toolbox: https://sites.google.com/site/bctnet/Home.
  10. WB_EEG_CalcLeadfield_standardBEM is a tool to generate conduction model of the head based on boundary element method (BEM) using standard MRI T1 image, and computes the forward model for many dipole locations on a 2D brain mesh or regular 3D grid and stores it for efficient inverse modelling using FieldTrip for EEG. The coordinates of head model is standard MNI space, and the electrodes will be automatically aligned later to the existing standard head model. Some codes obtained from FieldTrip 20181025 and EEGLAB were integrated. More details of network measures can be seen in the FieldTrip toolbox (http://www.fieldtriptoolbox.org/).

  • Dong, L., Li, F. L., Liu, Q., Wen, X., Lai, Y. X., Xu, P., & Yao, D. Z. (2017). MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG. Front Neurosci, 11. doi: Artn 601 10.3389/Fnins.2017.00601
  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003
  • Yao, D. (2001). A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas, 22(4), 693-711. 
  • Yao, D., Wang, L., Oostenveld, R., Nielsen, K. D., Arendt-Nielsen, L., & Chen, A. C. (2005). A comparative study of different references for EEG spectral mapping: the issue of the neutral reference and the use of the infinity reference. Physiol Meas, 26(3), 173-184. doi: S0967-3334(05)88258-9 [pii] 10.1088/0967-3334/26/3/003