NESTApp: A GUI-Based MATLAB Application for Auditable TMS-EEG Preprocessing with EEGLAB
- 1Institute of Mental Health Research at The Royal, Ottawa, ON, Canada
Abstract
Electroencephalography (EEG) preprocessing determines signal quality and interpretation, particularly for transcranial magnetic stimulation combined with EEG (TMS-EEG), where recordings are affected by large and complex stimulation-related artifacts. Several preprocessing approaches have been developed, but substantial variability remains in how pipelines are implemented, documented, and evaluated across recordings, limiting reproducibility and comparability across studies. We developed NESTApp, a MATLAB-based graphical interface built on EEGLAB, to provide an accessible and reproducible framework for implementing and evaluating TMS-EEG preprocessing pipelines. Rather than prescribing a specific pipeline, NESTApp allows users to construct and consistently apply customisable workflows incorporating established EEGLAB and TMS-EEG processing tools, while automatically documenting processing decisions and parameters. Integrated quality-control procedures track data removal and flag recordings based on user-defined criteria, facilitating systematic evaluation across participants. The application also supports group-level visualisation and export of processed TMS-EEG measures for subsequent analyses. Applied to 29 recordings from 16 participants, NESTApp processed 27 and identified those requiring further inspection. By integrating pipeline implementation, documentation, quality control, and group-level evaluation within a single interface, NESTApp provides a transparent framework for TMS-EEG preprocessing that may improve reproducibility, facilitate consistent processing across cohorts, and lower technical barriers to the implementation of established preprocessing approaches.
Related
- Association between hypertensive disorders of pregnancy and neurocognitive development in children aged 36 months: a cohort study in Matlab, Bangladesh
- A Structured, Auditable Taxonomy for Pharmacist-Identifiable Medication-Related Risk Signals in Routine Admission Data: A Retrospective Medication Safety Audit in a High-Risk Inpatient Cohort
- Resting-State Functional Connectivity in Antipsychotic-Naïve First-Episode Psychosis: A Systematic Review of Network Alterations and Preprocessing Heterogeneity
- Preprocessing-Induced Ranking Reversal is Architecture-Dependent in Cross-Domain Pneumonia Detection
- Homeostasis-Inspired, Quality-Adaptive Preprocessing for Multi-Label ECG Diagnosis: A Pre-Registered Multi-Objective Evolutionary Study with Negative Findings and Falsifiable Design Principles.
BibTeX
@misc{pariz2026nestapp,
title = {{NESTApp: A GUI-Based MATLAB Application for Auditable TMS-EEG Preprocessing with EEGLAB}},
author = {A. Pariz and W. Dunne and J. Lefebvre and S. Tremblay},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.11.26365251},
url = {https://www.medrxiv.org/content/10.64898/2026.10.11.26365251v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - NESTApp: A GUI-Based MATLAB Application for Auditable TMS-EEG Preprocessing with EEGLAB
AU - A. Pariz
AU - W. Dunne
AU - J. Lefebvre
AU - S. Tremblay
PY - 2026
DA - 2026/10/11
PB - medRxiv
DO - 10.64898/2026.10.11.26365251
UR - https://www.medrxiv.org/content/10.64898/2026.10.11.26365251v1
AB - Electroencephalography (EEG) preprocessing determines signal quality and interpretation, particularly for transcranial magnetic stimulation combined with EEG (TMS-EEG), where recordings are affected by large and complex stimulation-related artifacts. Several preprocessing approaches have been developed, but substantial variability remains in how pipelines are implemented, documented, and evaluated across recordings, limiting reproducibility and comparability across studies. We developed NESTApp, a MATLAB-based graphical interface built on EEGLAB, to provide an accessible and reproducible framework for implementing and evaluating TMS-EEG preprocessing pipelines. Rather than prescribing a specific pipeline, NESTApp allows users to construct and consistently apply customisable workflows incorporating established EEGLAB and TMS-EEG processing tools, while automatically documenting processing decisions and parameters. Integrated quality-control procedures track data removal and flag recordings based on user-defined criteria, facilitating systematic evaluation across participants. The application also supports group-level visualisation and export of processed TMS-EEG measures for subsequent analyses. Applied to 29 recordings from 16 participants, NESTApp processed 27 and identified those requiring further inspection. By integrating pipeline implementation, documentation, quality control, and group-level evaluation within a single interface, NESTApp provides a transparent framework for TMS-EEG preprocessing that may improve reproducibility, facilitate consistent processing across cohorts, and lower technical barriers to the implementation of established preprocessing approaches.
ER -