| name | mle-toolbox-eeg-meg |
| description | MLE-Toolbox: Comprehensive open-source MATLAB toolbox for end-to-end EEG/MEG analysis with source localization, connectivity analysis, and ML classifiers. Activation: MLE-Toolbox, EEG analysis, MEG analysis, source localization, brain network analysis, neuroimaging toolbox. |
MLE-Toolbox: Comprehensive EEG/MEG Analysis
Open-source MATLAB toolbox integrating full EEG/MEG analysis pipeline with preprocessing, source localization, functional connectivity, and machine learning classification.
Metadata
- Source: arXiv:2604.16463
- Authors: Xiaobo Liu
- Published: 2026-04-08
- Categories: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Core Methodology
Design Philosophy
Inspired by established neuroimaging platforms (Brainstorm, FieldTrip, EEGLAB), MLE-Toolbox provides unified, user-friendly GUI for complete EEG/MEG analysis workflow.
Full Analysis Pipeline
Raw Data → Preprocessing → Source Localization → Connectivity → ML Classification
Key Features
1. Preprocessing
Automated Artifact Rejection:
- Independent Component Analysis (ICA)
- Signal-Space Projection (SSP)
- Signal-Space Separation (SSS)
2. Source Localization Methods
Multiple inverse solutions:
- MNE: Minimum Norm Estimation
- dSPM: Dynamic Statistical Parametric Mapping
- sLORETA: Standardized Low-Resolution Brain Electromagnetic Tomography
- Beamforming: Adaptive spatial filtering
3. Parcellation & Visualization
- Multi-atlas parcellation
- Anatomical visualization
- Brain region segmentation
4. Spectral Analysis
- Spectral power analysis
- Frequency-band brain mapping
- Phase-Amplitude Coupling (PAC)
5. Connectivity Analysis
- Graph-theoretic brain network analysis
- Functional connectivity estimation
- Network topology metrics
6. Machine Learning
- Integrated classifiers
- Deep learning integration
- Feature extraction pipelines
Implementation Guide
Prerequisites
- MATLAB (R2018b or later recommended)
- Signal Processing Toolbox
- Statistics and Machine Learning Toolbox
- GPU support optional (for deep learning)
Installation
% Clone or download MLE-Toolbox
% Add to MATLAB path
addpath(genpath('/path/to/mle-toolbox'))
mle_startup
Basic Workflow
1. Data Import
% Import raw EEG/MEG data
data = mle_import('filename.eeg', 'format', 'eeglab');
2. Preprocessing
% Artifact rejection with ICA
[data_clean, ica_weights] = mle_ica_reject(data, 'n_components', 32);
% SSP for environmental noise
[data_clean] = mle_ssp(data_clean, 'n_projectors', 8);
3. Source Localization
% Compute forward model
headmodel = mle_headmodel(subject);
% Minimum norm estimation
sources = mne_solve(data_clean, headmodel, 'method', 'mne');
% Or beamforming
sources = mne_solve(data_clean, headmodel, 'method', 'beamformer');
4. Connectivity Analysis
% Compute connectivity matrix
conn_matrix = mle_connectivity(sources, 'method', 'pli');
% Graph theory metrics
metrics = mle_graph_metrics(conn_matrix, 'metrics', {'clustering', 'path_length'});
5. Machine Learning
% Train classifier
[model, accuracy] = mle_classify(features, labels, 'method', 'svm');
% Cross-validation
[model, cv_accuracy] = mle_classify_cv(features, labels, 'kfold', 10);
Advanced Features
Interoperability
Native compatibility with major platforms:
- Brainstorm: Import/export protocols
- FieldTrip: Data format conversion
- EEGLAB: .set file support
- FreeSurfer: Surface and volume import
Report Generation
One-click academic report generation:
mle_generate_report(analysis_results, 'format', 'pdf', 'template', 'neuroimage');
Interactive Visualization
% Brain surface visualization
mle_visualize_brain(sources, 'surface', 'pial', 'colormap', 'jet');
% Time-frequency plots
mle_plot_tfr(data, 'channels', {'Cz', 'Pz', 'Oz'});
% Connectivity graphs
mle_plot_connectivity(conn_matrix, 'atlas', 'aal');
Applications
Clinical Research
- Epilepsy source localization
- Stroke rehabilitation monitoring
- Sleep disorder analysis
Cognitive Neuroscience
- Working memory studies
- Attention network analysis
- Language processing research
BCI Development
- Motor imagery decoding
- P300 spellers
- Error-related potential detection
Drug Development
- Pharmacological EEG studies
- Biomarker discovery
- Treatment response monitoring
Comparison with Existing Tools
| Feature | MLE-Toolbox | Brainstorm | FieldTrip | EEGLAB |
|---|
| GUI | ✓ Integrated | ✓ | ✗ | ✓ |
| Source Loc. | MNE, dSPM, sLORETA, Beamforming | Dipole, MNE | Beamforming | Dipole |
| Connectivity | ✓ Graph-theoretic | ✓ | ✓ | Limited |
| ML Integration | ✓ Built-in | ✗ | ✗ | Limited |
| Report Gen | ✓ One-click | Manual | Manual | ✓ |
| Interoperability | All major tools | Limited | Limited | EEGLAB only |
Pitfalls
Common Issues
- Memory: Large datasets may require downsampling or chunking
- Head Models: Accurate source localization requires precise head models
- ICA Interpretation: Requires expertise to identify artifact components
Best Practices
- Always inspect raw data before preprocessing
- Use subject-specific head models when possible
- Validate source localization with known functional landmarks
- Apply appropriate statistical corrections for multiple comparisons
License
Freely available for non-commercial use.
Related Skills
- eeg-hopfield-emotion-energy
- eeg-structure-guided-diffusion
- functional-connectivity-graph-neural-networks
References
