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Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning.

Renzhou Gui1, Tongjie Chen1, Han Nie1

  • 1The Department of Information and Communication Engineering, Tongji University, Shanghai 201804, China.

Computational Intelligence and Neuroscience
|August 18, 2020
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Summary

This study introduces an improved circle-Empirical Mode Decomposition (circle-EMD) algorithm to enhance functional Magnetic Resonance Imaging (fMRI) data analysis for brain-computer interfaces. The enhanced method improves task-state classification accuracy using deep neural networks.

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Functional Magnetic Resonance Imaging (fMRI) data analysis for brain-computer interfaces faces challenges due to nonstationary signals and noise.
  • Accurate classification of human brain task states from fMRI is crucial for BCI research.

Purpose of the Study:

  • To propose an improved circle-Empirical Mode Decomposition (circle-EMD) algorithm to suppress end effects in fMRI signal analysis.
  • To enhance the accuracy of classifying different human brain task states using processed fMRI data.

Main Methods:

  • An improved circle-EMD algorithm was developed to decompose fMRI data and filter out noise, building upon the Hilbert-Huang Transform (HHT).
  • Filtered fMRI signals were classified using three machine learning models: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN).

Main Results:

  • The improved circle-EMD algorithm effectively suppressed noise and extracted intrinsic mode functions (IMFs) from fMRI data.
  • Deep neural networks (DNNs) achieved the highest accuracy in classifying task-state fMRI data compared to LR and SVM.

Conclusions:

  • The proposed improved circle-EMD algorithm is effective for preprocessing fMRI data in BCI applications.
  • Deep neural networks demonstrate superior performance for task-state classification of preprocessed fMRI data, highlighting the potential for advanced BCI systems.