Affective pattern analysis of image in frequency domain using the Hilbert-Huang Transform

Lee, Po-Ming; Tsui, Wei-Hsuan; Teng, Yun; Hsiao, Tzu‐Chien

doi:10.1109/apccas.2012.6419123

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…In 1999, Hilbert–Huang transform (HHT) [ 9 ], which is a combination of empirical mode decomposition (EMD) and Hilbert transform (HT), is an adaptive time-frequency analysis method and very suitable for feature extraction and analysis of the nonlinear and nonstationary signals [ 10 ]. HHT has good temporal and spatial resolution [ 11 – 13 ] and does not require a priori function basis [ 14 ], so that the original signal can be smoothed and decomposed to different scales of fluctuations and trends step by step [ 15 ]. The application field is very extensive [ 1 , 16 , 17 ] and very conducive to biomedical signal extraction [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

Gui

Chen

Nie

2020

Computational Intelligence and Neuroscience

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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.

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