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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

Matthias Guggenmos¹, Philipp Sterzer¹, Radoslaw Martin Cichy²

¹Visual Perception Laboratory, Charité Universitätsmedizin, Charitéplatz 1, 10117 Berlin, Germany.

|March 3, 2018

Summary

This study compares multivariate pattern analysis (MVPA) dissimilarity measures for magnetoencephalography (MEG) data. Noise normalization and cross-validated Euclidean distance are recommended for improved decoding accuracy and reliable representational similarity analysis (RSA).

Keywords:

Cross-validation Decoding EEG MEG Machine learning Multi-voxel pattern analysis Noise normalisation Representational similarity analysis

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Published on: June 14, 2010

Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning

Background:

Multivariate pattern analysis (MVPA) methods like decoding and representational similarity analysis (RSA) are increasingly used for magnetoencephalography (MEG) data.
The performance of various dissimilarity measures within these MVPA techniques is not well understood.

Purpose of the Study:

To characterize and compare the performance of different dissimilarity measures for decoding and RSA in MEG data.
To evaluate the impact of preprocessing and partitioning schemes on analysis outcomes.

Main Methods:

Utilized a multisession MEG dataset to assess dissimilarity measures from classifiers (LDA, SVM, WeiRD, GNB) and distances (Euclidean, Pearson correlation).
Investigated preprocessing steps (noise normalization, mean removal), decision value weighting, and partitioning schemes (non-cross-validated, cross-validated, within-class-corrected).

Main Results:

Multivariate noise normalization significantly enhanced decoding accuracy and dissimilarity measure reliability.
Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Weighted Robust Distance (WeiRD) showed high decoding accuracies.
Cross-validated Euclidean distance provided unbiased estimates and reliable representational dissimilarity matrices (RDMs).

Conclusions:

Recommends multivariate noise normalization as a standard preprocessing step for MEG MVPA.
Suggests LDA, SVM, and WeiRD as effective classifiers for decoding tasks.
Highlights cross-validated Euclidean distance as a reliable and unbiased default for RSA.