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Welcome to the MVPAlab wiki!. MVPAlab is a MATLAB-based and very flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution analyses. This toolbox has been designed to include an easy-to-use and very intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for those users with few or no previous coding experience. However, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.
Please cite the MVPAlab Toolbox reference paper when you have used MVPAlab for data analysis in your study:
López-García, D., Peñalver, J. M., Górriz, J. M., & Ruz, M. (2022). MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data. Computer Methods and Programs in Biomedicine, 214, 106549. https://doi.org/10.1016/j.cmpb.2021.106549
- Defining a configuration file
- Participants and data directories
- Trial average
- Balanced dataset
- Data normalization
- Data smoothing
- Analysis timing
- Dimensionality reduction
- Classification model
- Cross-validation
- Performance metrics
- Parallel computation
- Defining a configuration file
- Participants and data directories
- Trial average
- Balanced dataset
- Data normalization
- Data smoothing
- Analysis timing
- Channel selection
- Dimensionality reduction
- Classification model
- Cross-validation
- Performance metrics
- Parallel computation
- Sample EEG dataset
- Multivariate Pattern Analysis
- Multivariate Cross-Classification
- Temporal generalization matrix
- Feature contribution analysis
- Frequency contribution analysis
