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.

中文翻译：

---

基于Circle-EMD和机器学习的任务状态fMRI数据分类。

在脑机接口和人脑功能的研究工作中，多任务状态功能磁共振成像数据的状态分类是一个问题。人脑的fMRI信号是具有多种噪声影响和干扰的非平稳信号。基于常用的非平稳信号分析方法Hilbert-Huang变换（HHT），我们提出了一种改进的Circle-EMD算法来抑制端效应。该算法可以提取不同的固有模式函数（IMF），分解fMRI数据以滤除低频和其他冗余噪声信号，并更准确地反映原始信号的真实特征。对于滤波后的fMRI信号，我们使用三种现有的不同机器学习方法：逻辑回归（LR），支持向量机（SVM），和深度神经网络（DNN）来实现对不同任务状态的有效分类。实验比较了这些机器学习方法的结果，并证实了深度神经网络在任务状态fMRI数据分类中具有最高的准确性，并且改进的Circle-EMD算法的有效性。