Folder Structure

The MEGAP organizes its outputs within a structured directory system to streamline processing, analysis, and storage of MEG data. The top-level directory, named result, serves as the root for all pipeline-generated files and subfolders. Below is a detailed explanation of the structure and purpose of each folder:

/result/ 
└─── config/
    ├── eeglab-develop/
    ├── mne-matlab-master/
    ├── zapline-plus-main/
    ├── MegNET_2020-main/
    ├── Pipeline_config.cfg
    ├── Ct_sparse.fif
    └── sss_cal.dat
│
├── raw/
├── data/
├── PSD_data/
├── flat_channel/
├── head_position/
├── plot_head_pos/
├── filter_chpi/
├── PSD_filter_chpi/
├── zapline_plus/
├── PSD_zapline/
├── multitaper_removal/
├── PSD_multitaper_removal/
├── annotate_muscle/
├── plot_muscle_zscore/
├── bad_channel/
├── plot_bad_channel/
    ├── location/ 
    ├── score_mag/
    └── score_grad/
│
├── OTP/
├── PSD_OTP/
├── environment_noise/
├── PSD_environment_noise/
├── ICA/
├── PSD_ICA/
├── plot_ICA/
├── signal_before_ica/ 
    ├── Original/
    └── Zoomed/
│
├── signal_after_ica/
    ├── Original/
    └── Zoomed/
│
├── preprocessed_BIDS_output/
└── verbose/
└── warning/

Configuration Files: config/

The config folder contains essential files, repositories, and configuration parameters required to run the pipeline. Its contents include:

• eeglab-develop/: EEGlab repository from GitHub, essential for running Zapline_plus.

• mne-matlab-master/: MNE-MATLAB repository from GitHub for interfacing MEG data with MATLAB.

• zapline-plus-main/: Zapline-plus repository from GitHub for filtering line noise.

• MegNET_2020-main/: Pretrained MEGNet_2020 model for automatic artifact detection in ICA.

• Pipeline_config.cfg: The configuration file specifying parameters for running the pipeline.

• Ct_sparse.fif & sss_cal.dat: Calibration and crosstalk compensation files required for MEGIN system.

Raw Data: raw/

Contains raw MEG signals organized in the BIDS format, ensuring compatibility with standardized neuroimaging pipelines.

Intermediate Data

• data/: Stores output from the first pipeline step, with extraneous data removed.

• PSD_data/: Power spectral density (PSD) of the data.

• flat_channel/: Contains information about flat channels identified during pre-processing.

• head_position/: Tracks head movement data across recordings.

• plot_head_pos/: Visualizations of head position data.

• filter_chpi/: Contains filtered continuous head positioning indicator (cHPI) data.

• PSD_filter_chpi/: PSD results of post-cHPI filtering.

Line Noise Filtering

• zapline_plus/: Contains data processed with Zapline-plus in MATLAB for line noise removal.

• PSD_zapline/: PSD of data after Zapline-plus filtering.

• multitaper_removal/: Data processed using a regression-based multitaper method for line noise removal.

• PSD_multitaper_removal/: PSD of data after multitaper noise removal.

Detection and Annotation

• annotate_muscle/: Stores muscle artifact annotations generated during pre-processing.

• plot_muscle_zscore/: Z-score plots of magnetometer data for muscle artifact detection.

• bad_channel/: Contains information about identified bad channels.

• plot_bad_channel/: Visual representations of bad channels, including:

  • location/: Visuals of bad channel locations.

  • score_mag/: Magnetometer scores.

  • score_grad/: Gradiometer scores.

OTP Processing: OTP/

• Output from the OTP step, with PSD results stored in PSD_OTP/.

Environment Noise:

• environment_noise/: Results from the environment denoising process.

• PSD_environment_noise/: PSD results of environment denoising.

ICA: ICA/

• ICA/: Final Pre-processed Data in BIDS Format

• PSD_ICA/: PSD of data after ICA.

• plot_ICA/: ICA component plots for each participant, organized by participant ID.

Signal Plots

• signal_before_ica/: Data before ICA, stored in two formats:

  • Original/: Full-scale signal plots.

  • Zoomed/: Zoomed-in versions for detailed inspection.

• signal_after_ica/: Pre-processed data after ICA, with similar subfolders for original and zoomed signals.

Output Logs

• verbose/: Detailed log files generated during the pipeline's execution. These provide a record of processing steps and debug information for troubleshooting.

• warning/:Contains warning logs generated during the pipeline’s execution with the help of user threshold. Each file is named after the corresponding subject (e.g., sub_01.txt).