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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\u2013Huang 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.<\/jats:p>","DOI":"10.1155\/2020\/7691294","type":"journal-article","created":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T23:33:39Z","timestamp":1596324819000},"page":"1-10","source":"Crossref","is-referenced-by-count":6,"title":["Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5696-1220","authenticated-orcid":true,"given":"Renzhou","family":"Gui","sequence":"first","affiliation":[{"name":"The Department of Information and Communication Engineering, Tongji University, Shanghai 201804, 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