Features

General

  • Dedicated data file browser with Tag and Filter implementation.
  • Completely controlled through a Graphical User Interface : no knowledge of Matlab is required to use the toolbox.
  • Select and process multiple data files in one single step.

File Management

  • Copy datafiles
  • Move datafiles
  • Rename datafiles
  • Delete datafiles
  • Delete folder
  • Create folder

Import / Export datasets

  • Most import/export functions rely on the ft_read_header() and ft_read_data() functions of the Fieldtrip toolbox.
  • Import Brainvision VHDR files
  • Import EEPROBE/ASA CNT files
  • Import Micromed TRC files
  • Import Letswave 4 structured MAT files
  • Import EEGLAB SET files
  • Import Biosemi BDF files
  • Import CED Signal SGR files
  • Import ASCII (customizable format)
  • Import MAT
  • Import MAT files and variables
  • Export ASCII
  • Export EEGLAB SET files
  • Export Letswave 4 structured MAT files
  • Export MAT files and variables
  • Export Fieldtrip structure

Edit

  • Edit data file properties
  • Browse data file history
  • Generate methods
  • Add and Edit Tags (tags are used to mark a given set of data files)
  • Add and Edit Events (events are used to mark events such as stimulus onset, reaction times, ERP peaks, etc.)
  • Level trigger (similar to the BV Analyzer Level Trigger function to convert analog triggers to digital events)
  • Add and Edit Conditions (conditions are used to mark given epochs of a data file, for example, epochs corresponding to a given experimental condition)
  • Create conditions from events
  • Edit channel labels
  • Assign electrode locations (to generate 2D head plots of scalp topography)
  • Build spline files (to generate 3D head plots of scalp topography)
  • Check and fix file associations
    • Check and fix consistency of matrix files
    • Check and fix consistency of spline files
    • Check and fix consistency of headmodel files
    • Check and fix consistency of MRI data

Preprocess

  • Filters
    • FFT filter
    • Butterworth notch filter
    • Butterworth lowpass filter
    • Butterworth highpass filter
    • Butterworth bandpass filter
  • Rereference
  • Segmentation
    • Segmentation relative to events
    • Segmentation relative to events (SS-EPs)
    • Segmentation in successive chunks
  • Baseline operations
    • Baseline correction
    • Advanced baseline operations
    • Baseline operations using two datasets
    • Frequency spectrum SNR transforms
    • Z-score
  • Artefacts
    • Reject artefacted epochs (amplitude criterion)
    • Interpolate noisy electrodes
    • Suppress stimulation artefact
  • Frequency transforms
    • FFT
    • Inverse FFT
  • Time-frequency transforms
    • Continuous Wavelet Transform (CWT)
    • Short-term FFT
    • Fast CWT (as implemented in the previous versions of Letswave)
  • Time-frequency filter (these functions implement wavelet-based time-frequency filtering, as described in Hu et al. (NIMG, 2010)).
    • Build wavelet filter
    • Apply wavelet filter
  • Scalp Current Density (SCD)
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  • Independent Component Analysis (ICA)
    • Compute ICA matrix (runica) (See this link for additional information on the runica method)
    • Compute ICA matrix (jader) (See this link for additional information on the JADER method)
    • Assign ICA matrix file
    • Apply ICA filter
    • ICA unmix signals (channels>ICs)
    • ICA remix signals (ICs>channels)
  • Resample signals
    • Downsample signals
    • Crop signals
    • Resample (interpolate) signals
    • Chunk epochs
  • Arrange signals
    • Reorder/Delete epochs
    • Reorder/Delete channels
    • Reorder/Delete indexes
    • Merge epochs of multiple datasets
    • Merge channels of multiple datasets
    • Merge indexes of multiple datasets
    • Concatenate epochs from multiple datasets
    • Reorder/Delete epochs based on the presence of events
    • Select epochs based on the presence of events

Postprocess

  • Average
    • Average epochs
    • Average (pool) channels
    • Grand-average (weighted)
  • Single-trial analyses. (these functions implement methods to assess single-trial ERP amplitude and latency, as described in Hu et al. (NIMG, 2010) and Hu et al. (J Neurophysiol 2011)).
    • Multiple linear regresstion (MLR)
    • Multiple linear regression with distortion factor (MLRd)
  • Point-by-point Statistics. (these functions allow performing statistics to each bin of a given dataset. This is useful to generate statistic time courses or scalp maps. See Ronga et al. (J Neurophysiol 2012) for an example).
    • Compare two datasets
    • t-test
    • Wilcoxon test
    • Compare more than two datasets
    • Mixed-model ANOVA with the ability to include both within- and between-subject factors
    • Cross-correlation
  • Math
    • Mathematical operations using two datasets
    • Mathematical operations using a constant
    • Square signals
    • Rectify signals
    • Derivate
    • Variance Explained
    • Threshold
  • Source analysis. (these functions rely on the Fieldtrip source-analysis functions).
    • Dipole fitting (using the Fieldtrip ft_dipolefitting() function)
    • Assign channel locations
    • Assign 3-shell head model
    • Assign MRI data
    • Fit dipole(s) at event latencie(s)
    • Fit dipole(s) onto Independent Components (ICs)
    • Plot dipoles

View

  • Wave multiviewer
  • Wave continuous viewer
  • Wave scalp array
  • Map multiviewer
  • Peak viewer
  • Conditions viewer
  • Events viewer
  • Figures
  • Wave figure
  • Map series figure
  • Peak figure
  • Phase figure
  • Conditions figure