An open-source toolbox for Multi-patient Intracranial EEG Analysis (MIA)

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Epilepsy Research

Background:

• Intracranial EEG (iEEG) offers high-resolution insights into human brain function during epilepsy pre-surgical evaluation.
• Analyzing iEEG data across multiple patients presents challenges due to anatomical and functional variability.
• Standardized, reproducible analysis methods are crucial for advancing cognitive neuroscience with iEEG.

Purpose of the Study:

• To introduce and validate MIA (Multi-Patient Intracranial data Analysis), an open-source toolbox for iEEG data analysis.
• To provide a user-friendly, efficient platform for reproducible cognitive neuroscience research using iEEG.
• To enable robust statistical analysis and visualization of event-related iEEG signals across patients.

Main Methods:

• Development and implementation of the MIA toolbox, usable as a standalone program or Brainstorm plugin.
• Analysis of event-related iEEG signals in temporal and time-frequency domains.
• Assessment of signal similarity across patients and anatomical regions, with comprehensive visualization tools.

Main Results:

• MIA provides a reproducible framework for analyzing multi-patient iEEG data.
• The toolbox supports detailed analysis of temporal and time-frequency signal characteristics.
• Demonstrated utility of MIA using a language task dataset, highlighting its visualization capabilities.

Conclusions:

• MIA enhances the analysis of iEEG data for cognitive neuroscience, addressing inter-patient variability.
• The open-source toolbox promotes good scientific practice through reproducible pipelines and robust statistics.
• MIA facilitates deeper understanding of human cognitive neurophysiology using pre-surgical iEEG.