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

Abstract EN

In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem.

The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference.

Based on the commonly used nonstationary signal analysis method, Hilbert–Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect.

The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal.

For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states.

The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm.