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EEGTOOLS

Complete EEG analysis (ERP, ERSP, Sources) pipeline written in matlab, using EEGLAB and BRAINSTORM

Description

EEGTools was developed in the Robotics, Brain and Cognitive Sciences (RBCS) department of the Istituto Italiano di Tecnologia (IIT) of Genova, Italy. It has been equally developed by Alberto Inuggi and Claudio Campus, PostDoc Researchers currently working for the U-VIP (https://www.iit.it/lines/unit-for-visually-impaired-people) unit. It is a huge set of matlab scripts allowing a complete analysis of the Electroencephalography (EEG) signal at either the sensor and source levels. It implement all the analysis steps relying on the EEGLab (https://sccn.ucsd.edu/eeglab), Brainstorm (http://neuroimage.usc.edu/brainstorm) and FieldTrip (http://www.fieldtriptoolbox.org)software primitives.

Why use it

Every analysis framework has a learning curve which must be matched with the advantages it provides to the user. eegtools offer several features (see next paragraph), most of them can be found in many other software packages. Its unicity is the possibility to model all the characteristics of a huge and complex EEG project and perform automatic analysis. All the analysis steps (but for artefact removal) can be performed in batch mode (code, start, go home and find everything processed next morning). Once you create your clean continuous files, you can concatenate all the steps from epoching to group stats, plotting and results export. This is granted by compiling a huge project_structure.m file, where you can define eeg data characteristics, preprocessing params, participants details, statistical models, electrodes cluster, time window, frequency bands, analysis types and many other features. Moreover, it eases the integration between EEGLab and Brainstorm and allows calling the latter methods, and many others custom methods, from matlab command line using the same data structure used for ERP/ERSP analysis.

Features

  • Subject level

    • Import raw files (BrainVision, Biosemi, Geodesic, XDF, EDF)
    • Filtering
    • Referencing
    • Channel transformation (e.g. bipolar creation)
    • Channel rectification
    • HR calculation from EKG channel
    • Channel interpolation
    • Subsampling
    • Epoching & Baseline correction
    • Triggers manipulation
    • Trigger extraction from deflection on analog channel using absolute or statistical threshold (possibly distinguihing triggers based on deflection durations)
    • ICA & CUDAICA
    • ICLABEL classification
    • ASR
    • Merge different montages
    • Microstates
    • Data driven selection of regions of interest and times
    • Export to EDF format
    • Comparing conditions (ERP/ERSP) at subject level
    • Export to RAGU and preprocessing using RAGU, e.g. TAN(C)OVA, microstates.
  • Group level

    • EEGLab Study creation, design and compute measure
    • ERP & ERPS Stats (continuous curve, time-windows, time-frequency, frequency-bands, topo-plot, peak-analysis, individual align, individual narrow-band.. etc...)
    • Plotting
    • Result export
  • Source analysis

    • EEGLab epochs file import
    • Noise and data covariance estimation
    • BEM creation
    • Individual Source calculation (time, time-frequency, scouts)
    • Source dimensionality reduction (time, space)
    • Z-score, Norm, flattening
    • Source results export to SPM8/12
    • Stats (2-samples paired t-test, scouts stats, TF stats)
    • Results export
  • Sleep analysis

    • Import files in Hume toolbox for sleep staging/analyses
    • Import Hume staging into EEGLab and Hume analyses for futher processing
    • Insert custom marks (e.g. spindles, k-complexes) and sleep staging (alternative to Hume)
    • Convert sleep marks into EEGLab events (for a better integration in EEGLab processing)
    • Add a sleep stage field to the EEG.event structure to label the sleep stage of each event
    • Computation of spectrogram and sleep statistics
    • Automatic detection of sleep spindles and k-complexes (using HUME and/or EEGLab toolbox) and export of related features (e.g. duration) in textual format

First-time Installation

These steps are required only the first time you setup this pipeline

  • Download and install both EEGLab (https://sccn.ucsd.edu/eeglab/downloadtoolbox.php) and Brainstorm (http://neuroimage.usc.edu/bst/download.php) software. Rename them to eeglab and brainstorm3 respectively.
  • 1: Copy the present repositories anywhere in your file system (referred as GLOBAL_SCRIPT_ROOT)
  • 2: Create a root folder where you will put the data (eeg raw files, eeglab set/fdt files and your brainstorm database) of your EEG projects (referred as PROJECTS_DATA_ROOT).
  • 3: Create a root folder where you will put all the matlab script of your EEG projects (referred as PROJECTS_SCRIPTS_ROOT)

Add to Matlab path the GLOBAL_SCRIPT_ROOT path. All the other paths will be added by the pipeline scripts.

How to setup a new project

These steps are required each time you want to setup a new EEG project

  • Create the project data folder : PROJECTS_DATA_ROOT/PROJ_NAME
  • Create the project script folder : PROJECTS_SCRIPTS_ROOT/PROJ_NAME
  • Copy the following three files from GLOBAL_SCRIPT_ROOT/eeg_tools/templates => PROJECTS_SCRIPTS_ROOT/PROJ_NAME
    • template_project_structure.m
    • template_main_eeglab_subject_processing.m
    • template_main_eeglab_group_processing.m
  • Rename these files as wish and edit them as later explained
  • Create the folder PROJECTS_DATA_ROOT/PROJ_NAME/original_data and copy there all your raw eeg files

Usage

In order to analyze an EEG project you will need at least 3 files.

  • A Project Structure file: which describe the data characteristic and all the necessary parameters to perform the analysis
  • A main_eeglab_subject_processing.m file which allows analyzing each individual subject
  • A main_eeglab_group_processig.m file which define the study, the experimental design and allows performing the requested analysis

Two two main files starts asking you to fill in the following information:

  • project.paths.projects_data_root = '/media/data/EEG_projects_data';
  • project.paths.projects_scripts_root = '/media/data/EEG_projects_scripts';
  • project.paths.global_scripts_root = '/media/data/EEG/eegtools/matlab-pipeline';
  • project.paths.plugins_root = '/media/data/matlab/toolboxes';

PROJECT DATA

  • project.research_group = 'MY_DEPARTMENT_GROUP';
  • project.research_subgroup = 'MY_LAB';
  • project.name = 'my_project';
  • project.conf_file_name = 'project_structure';

The last params identify the project structure file, a huge (up to 1500 lines) files containing variables definitions.

Publications using it

13 . Amadeo M.B., Campus C., Gori M., Years of Blindness Lead to Visualize Space Through Time, Frontiers in Neuroscience, vol. 14, 2020, 10.3389/fnins.2020.00812

12 . Amadeo M.B., Campus C., Gori M., Visual representations of time elicit early responses in human temporal cortex, NeuroImage, vol. 217, 2020, 10.1016/j.neuroimage.2020.116912

11 . Gori M., Amadeo M.B., Campus C., Temporal cues trick the visual and auditory cortices mimicking spatial cues in blind individuals, Human Brain Mapping, vol. 41, (no. 8), pp. 2077-2091, 2020, 10.1002/hbm.24931

10 . Maffongelli L., Ferrari E., Bartoli E., Campus C., Olivier E., Fadiga L., D'Ausilio A., Role of sensorimotor areas in early detection of motor errors: An EEG and TMS study, Behavioural Brain Research, vol. 378, 2020, 10.1016/j.bbr.2019.112248

9 . Campus C., Sandini G., Amadeo M.B., Gori M., Stronger responses in the visual cortex of sighted compared to blind individuals during auditory space representation, Scientific Reports, vol. 9, (no. 1), 2019, 10.1038/s41598-018-37821-y

8 . Marini F., Zenzeri J., Pippo V., Morasso P., Campus C., Neural correlates of proprioceptive upper limb position matching, Human Brain Mapping, pp. 1-14, 2019, 10.1002/hbm.24739

7 . Amadeo M.B., Stormer V.S., Campus C., Gori M., Peripheral sounds elicit stronger activity in contralateral occipital cortex in blind than sighted individuals, Scientific Reports, vol. 9, (no. 1), 2019, 10.1038/s41598-019-48079-3

6 . Amadeo M.B., Campus C., Gori M., Impact of years of blindness on neural circuits underlying auditory spatial representation, NeuroImage, vol. 191, pp. 140-149, 2019, 10.1016/j.neuroimage.2019.01.073

5 . Deloglu F, Brunetti R, Inuggi A, Campus C, Del Gatto C, D'Ausilio A. That does not sound right: Sounds affect visual ERPs during a piano sight-reading task. Behavioural Brain Research, 2019. (ERP + source analysis)

4 . Inuggi A, Campus C, Vastano R, Keuroghlanian A, Saunier G, Pozzo T. Locomotion observation induces motor resonance only when explicitly represented; An EEG source analysis study. Frontiers in Psychology, 2018. (ERP + source analysis)

3 . Inuggi A, Bassolino M, Tacchino C, Pippo V, Bergamaschi V, Campus C, De Franchis V, Pozzo T, Moretti P. Ipsilesional functional recruitment within lower mu band in children with unilateral cerebral palsy , an event-related desynchronization study", Experimental Brain Research. 2017. (ERSP)

2 . Pozzo T, Inuggi A, Keuroghlanian A, Panzeri S, Saunier G, Campus C. Natural Translating locomotion modulates cortical activity at action observation. Frontiers in Systems Neuroscience. 2017 (ERSP)

1 . Campus C, Sandini G, Morrone MC, Gori M. Spatial localization of sound elicits early responses from occipital visual cortex in humans. Sci Rep. 2017; 7: 10415. (ERP)


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