This topic traces the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data from raw recordings to interpretable results. You will learn to inspect and preprocess raw recordings, analyze event-related potentials, apply time-frequency analysis and source separation, and explore advanced methods such as connectivity analysis and selective-attention decoding. Using simulations and real-world data, you will connect theoretical foundations in signal processing, statistics, and machine learning to practical workflows in MNE-Python and the scientific Python ecosystem.
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EEG and MEG provide non-invasive views of neural activity with millisecond-scale temporal resolution. EEG measures electrical potential differences at the scalp, whereas MEG measures magnetic fields associated with neural currents. In both modalities, each sensor captures a mixture of activity from multiple neural sources, making the relationship between recorded signals and underlying processes an important part of the analysis.
Analyzing EEG and MEG data requires careful alignment of experimental events, sensor recordings, preprocessing decisions, and statistical interpretations. MNE-Python provides a powerful open-source toolkit, but scientifically sound conclusions depend on understanding how choices about artifact handling, epoching, time-frequency analysis, source separation, connectivity, and decoding affect the results. This topic connects these concepts and theoretical foundations to transparent, reproducible computational workflows.
For this approach, familiarity with Python and Jupyter notebooks is required. Consult the event page for its specific setup and requirements.
Using simulated and real-world recordings, you will examine this relationship while developing reproducible analysis workflows. Practical examples use MNE-Python alongside scientific Python libraries such as NumPy, Matplotlib, Scikit-learn, and mTRFpy to organize and visualize recordings, link signals to experimental events, compare conditions, and evaluate models of neural responses.
Explore upcoming dates and details from previous events.
This four-week online course covers preprocessing and visualizing raw EEG and MEG recordings with MNE-Python, statistical analysis of event-related potentials, source separation, …
Would you like to learn more about (M)EEG analysis and programming in Python? By the course's end, you’ll have an understanding of how to install and setup a Python environment …
Would you like to learn more about (M)EEG analysis and programming in Python? By the course's end, you’ll have an understanding of how to install and setup a Python environment …
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Check important behavior automatically so research code can be changed with confidence.
Practices · Teaching
0 topics with recorded teaching · 0 recorded and scheduled offerings
Tests make scientific assumptions and expected behavior explicit, and help catch regressions as analysis code develops.
Covered in our robust software and AI-assisted development topics; individual offerings are not yet attributed to this practice.
0 past offerings · 0 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: AI-Assisted Software DevelopmentMachine Learning(M)EEG AnalysisNeural Spike AnalysisRobust and Reusable Software
Check the shape, types, units, and assumptions of research data before using it.
Practices · Teaching
0 topics with recorded teaching · 0 recorded and scheduled offerings
Explicit validation turns data assumptions into checks that can catch missing values, inconsistent metadata, and unexpected inputs before analysis.
Covered through data organization and robust software topics; individual offerings are not yet attributed to this practice.
0 past offerings · 0 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: Programming and Data AcquisitionAI-Assisted Software DevelopmentArrays and Numerical ComputingCalcium Imaging AnalysisDataframes and StatisticsLocal Field Potential (LFP) AnalysisMachine Learning(M)EEG AnalysisNeural Spike AnalysisRobust and Reusable Software
Hierarchical Data Format for storing large scientific datasets
Data formats · Not currently taught
0 topics with recorded teaching · 0 recorded and scheduled offerings
HDF5 is the standard format for storing large, complex scientific datasets with rich metadata. Its hierarchical structure and efficient compression make it ideal for neuroscience data like multi-channel recordings and imaging time series.
Relevant to the topic landscape; no explicit recorded offering is currently attributed to this item.
0 past offerings · 0 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: Arrays and Numerical ComputingCalcium Imaging AnalysisDataframes and Statistics(M)EEG Analysis
Comprehensive library for creating static, animated, and interactive visualizations
Libraries · Teaching
6 topics with recorded teaching · 19 recorded and scheduled offerings
Matplotlib is the foundational plotting library for Python, offering complete control over every aspect of your figures. It's publication-ready out of the box and integrates seamlessly with NumPy and Pandas, making it essential for visualizing research data.
Recorded in topic offerings; consult the dated event links for availability.
15 past offerings · 4 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: AI-Assisted Software DevelopmentArrays and Numerical ComputingDataframes and StatisticsDeveloper EnvironmentLocal Field Potential (LFP) Analysis(M)EEG AnalysisNeural Spike Analysis
Open-source Python package for exploring, visualizing, and analyzing MEG and EEG data
Libraries · Teaching
1 topics with recorded teaching · 3 recorded and scheduled offerings
MNE-Python provides a comprehensive, well-documented toolkit for processing electrophysiological data. It handles the full analysis pipeline from raw recordings to publication-ready results, with strong support for time-frequency analysis, source localization, and connectivity measures.
Recorded in topic offerings; consult the dated event links for availability.
2 past offerings · 1 upcoming offerings
Related themes: Neural Data Analysis
Related topics: Local Field Potential (LFP) Analysis(M)EEG Analysis
The fundamental package for scientific computing with Python
Libraries · Teaching
8 topics with recorded teaching · 26 recorded and scheduled offerings
NumPy is the foundation of the scientific Python ecosystem. It provides powerful n-dimensional array objects and tools for working with these arrays. Nearly every scientific Python package builds on NumPy, making it essential for data analysis, numerical computing, and scientific research.
Recorded in topic offerings; consult the dated event links for availability.
21 past offerings · 5 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: AI-Assisted Software DevelopmentArrays and Numerical ComputingCalcium Imaging AnalysisDataframes and StatisticsLocal Field Potential (LFP) AnalysisMachine Learning(M)EEG AnalysisNeural Spike AnalysisRobust and Reusable Software
Standardized format for neuroscience data sharing and archival
Data formats · Not currently taught
0 topics with recorded teaching · 0 recorded and scheduled offerings
NWB is the emerging standard for neuroscience data sharing, required by many NIH BRAIN Initiative grants. It provides a unified format for diverse data types (ephys, imaging, behavior) with rich metadata, enabling tool interoperability and compliance with FAIR principles.
Relevant to the topic landscape; no explicit recorded offering is currently attributed to this item.
0 past offerings · 0 upcoming offerings
Related themes: Neural Data Analysis
Related topics: Calcium Imaging AnalysisLocal Field Potential (LFP) Analysis(M)EEG AnalysisNeural Spike Analysis
General-purpose programming language widely used for scientific computing and research software.
Languages & frameworks · Teaching
10 topics with recorded teaching · 33 recorded and scheduled offerings
Python combines a readable language with a mature scientific ecosystem, making it a practical default for research data analysis, automation, and teaching.
Recorded in topic offerings; consult the dated event links for availability.
26 past offerings · 7 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: Programming and Data AcquisitionAI-Assisted Software DevelopmentArrays and Numerical ComputingAutomating Research WorkflowsCalcium Imaging AnalysisDataframes and StatisticsDeveloper EnvironmentLocal Field Potential (LFP) AnalysisMachine Learning(M)EEG AnalysisNeural Spike AnalysisRobust and Reusable Software
Machine learning library for Python with tools for classification, regression, clustering, and dimensionality reduction
Libraries · Teaching
3 topics with recorded teaching · 6 recorded and scheduled offerings
Scikit-learn provides a consistent, easy-to-use interface for machine learning in Python. It's perfect for analyzing neuroscience data: clustering neurons, reducing dimensionality of neural responses, and classifying behavioral states.
Recorded in topic offerings; consult the dated event links for availability.
4 past offerings · 2 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: AI-Assisted Software DevelopmentMachine Learning(M)EEG AnalysisNeural Spike Analysis
Scientific computing library providing optimization, signal processing, and statistical functions
Libraries · Teaching
5 topics with recorded teaching · 14 recorded and scheduled offerings
SciPy builds on NumPy to provide a comprehensive collection of algorithms for scientific computing. Its signal processing tools are essential for analyzing neural recordings, while its optimization and statistical functions support data analysis and modeling across neuroscience research.
Recorded in topic offerings; consult the dated event links for availability.
9 past offerings · 5 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: AI-Assisted Software DevelopmentArrays and Numerical ComputingCalcium Imaging AnalysisLocal Field Potential (LFP) AnalysisMachine Learning(M)EEG AnalysisNeural Spike Analysis
Python package for working with electrophysiology data, providing common data structures and I/O for various formats
Libraries · Teaching
1 topics with recorded teaching · 1 recorded and scheduled offerings
Neo provides standardized data structures for electrophysiology that bridge different recording systems and analysis tools. It handles metadata, units, and relationships between data types, making your analysis code more portable and maintainable.
Recorded in topic offerings; consult the dated event links for availability.
1 past offerings · 0 upcoming offerings
Related themes: Neural Data Analysis
Related topics: Calcium Imaging AnalysisLocal Field Potential (LFP) Analysis(M)EEG AnalysisNeural Spike Analysis
Statistical functions and probability distributions from SciPy's stats module.
Libraries · Teaching
1 topics with recorded teaching · 1 recorded and scheduled offerings
SciPy Stats provides a broad, scriptable foundation for distributions, statistical tests, random variables, and summary statistics used throughout scientific Python workflows.
Recorded in topic offerings; consult the dated event links for availability.
0 past offerings · 1 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: Arrays and Numerical ComputingDataframes and StatisticsLocal Field Potential (LFP) Analysis(M)EEG Analysis
N-dimensional labeled arrays and datasets for scientific data analysis
Libraries · Teaching
2 topics with recorded teaching · 2 recorded and scheduled offerings
Xarray extends pandas-like labels and indexing to multi-dimensional arrays, making it perfect for working with complex scientific datasets like electrophysiology recordings, imaging data, and climate data. It handles coordinates, dimensions, and metadata elegantly.
Recorded in topic offerings; consult the dated event links for availability.
1 past offerings · 1 upcoming offerings
Related themes: Neural Data AnalysisScientific Computing
Related topics: Arrays and Numerical ComputingDataframes and Statistics(M)EEG AnalysisRobust and Reusable Software
Python package for fitting and evaluating multivariate temporal response function models for continuous stimulus-response data
Libraries · Teaching
1 topics with recorded teaching · 2 recorded and scheduled offerings
Assess whether this fits the group’s research and teaching workflows. mTRFpy brings temporal response function modeling to Python. It supports regularized forward and backward models, cross-validation, prediction, visualization, and integration with MNE-Python, making it well suited to reproducible analyses of continuous neurophysiological data.
Recorded in topic offerings; consult the dated event links for availability.
1 past offerings · 1 upcoming offerings
Related themes: Neural Data Analysis
Related topics: (M)EEG Analysis
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