DOCUMENTATIONS
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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). Currently, REST is increasingly acknowledged by EEG/ERPs community around the world (to our knowledge, at least 12 countries/areas), and more than 50 studies have actually adopted REST to get zero reference as the foundation of their novel findings. Meanwhile, the REST has been regarded as the Rosetta Stone for scalp EEG (Kayser & Tenke, 2010) and listed in the new guidelines of International Federation of Clinical Neurophysiology (IFCN) for EEG analysis. More details about REST toolbox can also be seen in the paper (Dong et al., 2017).
  2. 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’. If the input ‘eventlabel’ is empty, all data will be used. If applicable, EEG segments in bad block (label 9999, marked by the tool WB_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 time-frequency analysis with Fast-Fourier Transform (FFT) to obtain the absolute EEG band power at each electrode in the specific bands. Each data epoch will be linearly detrended before time-frequency analysis.
  3. WB_EEG_Mark is a tool to automatically mark bad block/good quality EEG data based on thresholding z-scores/global field power. 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. Per channel/electrode every time point is z-normalized (mean subtracted and divided by standard deviation). Or the standard deviation (global field power, GFP) of the signal at all electrodes is calculated. [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. Bad blocks are labeled by ‘9999’ (EEG.event.type is ‘9999’, percentage (absolute value) above threshold > 1% for each small epoch). Good quality data are labeled by ‘2001’ (EEG.event.type is ‘2001’, percentage (absolute value) above threshold < 5% for each small epoch). If bad blocks with label ‘9999’ already existed, the bad block data will be NOT marked as good quality data.
  4. 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’. 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 be rejected automatically, and NOT used to calculate network. [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.
  5. 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. This step is optional, and default is no filtered (i.e. set passband as []). [2] Extract epochs (default is [-0.2 0.8] sec) and baseline correction ( [-0.2, 0] sec): A continuous EEG dataset will be converted to epoched data by extracting data epochs time locked to specified event types or event indices. If applicable, time locked events corresponding to correct-reaction marker will be extracted (i.e. marker1). In addition, events in bad block (label 9999, marked by WB_EEG_Mark) will also be rejected automatically. [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).
  6. 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.
  7. 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.

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  • References
  • 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 60110.3389/Fnins.2017.00601
  • Kayser, J., & Tenke, C. E. (2010). In search of the Rosetta Stone for scalp EEG: converging on reference-free techniques. Clin Neurophysiol, 121(12), 1973-1975. doi: 10.1016/j.clinph.2010.04.030
  • 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