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Related Experiment Video

Updated: May 22, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.

Jessica Schrouff1, Caroline Kussé, Louis Wehenkel

  • 1Cyclotron Research Centre, University of Liège, Liège, Belgium. jschrouff@doct.ulg.ac.be

Plos One
|May 8, 2012
PubMed
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Classifying brain activity from functional magnetic resonance imaging (fMRI) data is challenging, especially with self-generated mental states. Gaussian Processes (GP) showed more robustness than Support Vector Machines (SVM) for imbalanced fMRI datasets.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Machine Learning

Background:

  • Predicting cognitive states from fMRI voxel patterns presents methodological challenges.
  • Traditional fMRI decoding relies on controlled experiments with distinct, externally imposed mental states.
  • Realistic conditions involve self-generated mental states leading to complex, imbalanced fMRI data.

Purpose of the Study:

  • To test classification techniques for decoding brain activity during self-generated mental imagery.
  • To identify optimal procedures for accurate fMRI data classification in non-controlled conditions.
  • To compare the performance of Support Vector Machines (SVM) and Gaussian Processes (GP) on imbalanced fMRI datasets.

Main Methods:

  • fMRI data acquired from 16 volunteers during self-generated mental imagery.

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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

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Last Updated: May 22, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

  • Employed Support Vector Machines (SVM) and Gaussian Processes (GP) for classification.
  • Utilized General Linear Model (GLM) and SVM for feature extraction.

    • Main Results:

    • 12 out of 16 datasets were significantly modeled using either SVM or GP.
    • Classification accuracy correlated with data class imbalance and individual task performance.
    • Gaussian Processes (GP) demonstrated greater robustness in modeling imbalanced fMRI data compared to SVM.

    Conclusions:

    • fMRI brain activity decoding is feasible even with self-generated mental states.
    • Gaussian Processes (GP) offer a more robust approach for analyzing imbalanced fMRI datasets.
    • The choice of classification and feature extraction methods impacts decoding accuracy in realistic cognitive paradigms.