Welcome to Share Your Tools in WeBrain!

DOCUMENTATIONS
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Current Supported EEG Data Formats:
üASCII/Float file (*.txt)
üMATLAB (*.mat/*.dat)
üEEGLAB ({*.set, *.fdt} or *.fdt)
üCurry 6/7 ({*.dat, *.dap, *.rs3})
üCurry 8/9 ({*.cdt, *.cdt.ceo, *.cdt.dpa})
üBrain Products/Brain Vision ({*.vhdr, *.vmrk, *.dat} or {*.vhdr, *.vmrk, *.eeg})
üNeuroScan(*.cntor *.EEG)
üBiosemi/European Data Format (*.bdfor *.edf)
üBIOSIG (*.edf, *.edf+, *.gdfor *.bdf)
üEGI MFF file (.mff)
ü博瑞康NeuracleEEG Recorder ({data.bdf,evt.bdf})
üANT (*.cnt)
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WeBrain Pipeline List

  1. WB_EEG_RESTis 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_Markis 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_runICAis 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_QAis 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). The QA consists of : [1] A continuous EEG data of each channel will be high pass filtered (>1Hz) and then segmented as small windows; [2] Detecting constant or NaN/Inf signals in each window (Method 1). [3] Detecting unusually high or low amplitude using robust standard deviation across time points in each window (Method 2). [4] Detecting high or power frequency noises in each window by calculating the noise-to-signal ratio (NSR) based on Christian Kothe's method (Method 3). [5] Detecting low correlations with other channels in each window using Pearson correlation (default) or RANSAC correlation (Method 4). [6] Calculating a number of indices relative to the data quality and rating the EEG raw data.
  5. WB_EEG_preprois 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.It is supported to preprocess EEG raw data with single point, average or linked LM 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 (Dong et al., 2021; Perrin et al., 1989) 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_prepro_cmis 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.It is supported for the EEG raw data with a specific non-unipolar recording montage, such as the ipsilateral mastoid (IM) or the contralateral mastoid (CM).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 (>6) using z-scored STD across channels, and then clean EEG data with REST reference are obtained finally.
  7. WB_EEG_CalcPoweris 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.
  8. WB_EEG_CalcERPis 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_CalcNetworkis a basic tool to calculate EEG network between EEG channels at scalp level or source 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
  10. WB_EEG_CalcNetMeasuresis 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 and Sporns, 2010) and BCT toolbox: https://sites.google.com/site/bctnet/Home.
  11. WB_EEG_CalcLeadfield_standardBEMis 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/).
  12. WB_EEG_sourceimageis a tool to estimate source signals of scalp EEG/ERP data based on a forward model and inverse method (e.g. sLORETA). Source imaging estimation consists of: [1] Loading EEG data and check the items including data, channel locations, and sampling rate. [2] Calculating the leadfield matrix by solving forward problem based on selected head model and channel locations. Or obtaining a user defined leadfield matrix. [3] If needed, passband filtering the EEG data. Default is no filtering. [4] Specific event data can be extracted according to the input ‘eventlabel’. If the input ‘eventlabel’ is empty, all data will be used. [5] EEG data of each epoch (default is 5-s epoch) is subjected to estimate sources to obtain the EEG signals in the source space using an inverse method such as ‘sLORETA’ (Dale et al., 2000; Pascual-Marqui, 2002). [6] If needed, matching source signals to a brain template (e.g. AAL template) to obtain the averaged source signals of brain regions. [7] Saving the results of EEG source signals and parameters as a .set file.
  13. WB_EEG_calcLZCis a tool to calculate Lempel-Ziv Complexity indices for each EEG channel. “Complexity” is a widely used nonlinear dynamic concept in the EEG analysis. Because the complexity analysis mainly represents the degree of randomness in time series, the complexity of EEG data measures the capacity of information in the EEG signal fragment and then may reflect the underlying activeness of the neurons. Lempel-Ziv complexity was proposed by Lempel and Ziv and along with its derivatives has found numerous applications in characterizing the randomness of biological signals, especially in EEG analysis.
  14. WB_EEG_timefreqis a tool to conduct time-frequency analysis to reveal time-varying spectrum of non-stationary EEG signals. It will calculate time-frequency spectrum and inter-trial coherence (ITC) events across event-related trials (epochs) of each channel time series. More details can be seen in EEGLAB function, newtimef() or timefreq().
  15. WB_EEG_calcMicrostateis a tool to conduct microstate analysis for resting-state EEG data or ERP data. It is used to offer a sparse characterisation of the spatio-temporal features of large-scale brain network activity. It will calculate microstate indices across event-related trials (epochs) of time series. Calculating microstate indices consists [1] Specific event data can be extracted according to the input ‘eventlabel’. If the input ‘eventlabel’ is empty, all data will be used. If applicable, EEG segments in bad block (label 9999, marked by wb_pipeline_EEG_Mark) will also be rejected automatically, and NOT used to calculate power indices. [2] Specific event EEG signals will be divided into small epochs. [3] EEG data of each epoch (default is 5s epoch) was subjected to microstate analysis to obtain the quantitative indices of the presence of microstates (1 × classes) in each EEG data.
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References:
  • Dale, A.M., et al., 2000. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron. 26, 55-67.
  • Dong, L., et al., 2017. MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG. Frontiers in Neuroscience. 11.
  • Dong, L., et al., 2021. Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG. Brain Topography.
  • Pascual-Marqui, R.D., 2002. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 24 Suppl D, 5-12.
  • Perrin, F., et al., 1989. Spherical splines for scalp potential and current density mapping. Electroencephalogr Clin Neurophysiol. 72, 184-7.
  • Rubinov, M., Sporns, O., 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 52, 1059-69.
  • Yao, D., 2001. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas. 22, 693-711.
  • Yao, D., et al., 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, 173-84.

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