Analysis and Feature Extraction of Laser Micro-Doppler Signatures Based on Hilbert-Huang Transforms

Mingjin, 彭明金 Peng; Zhi, 李智 Li

doi:10.3788/cjl201340.0809004

Cited by 2 publications

(1 citation statement)

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“…起的血液血氧指标波动 [23] 。采用带通滤波和基于 HbO 与 Hbb 信号相关性的信号改善(CBSI)算法消除信号干扰。带通滤波的频带范围为 0.05~0.5 Hz，主要滤除生理干扰和高频噪声 [24] 。基于 HbO 与 Hbb 信号相关性的改进 CBSI 算法是根据两种不同血氧蛋白信号之间的相关性消除突发运动干扰 [25] 。CBSI 方法是 Cui 等 [26] 提出的一种逐通道滤除运动伪迹的方法，这种方法建立在 HbO 和 Hbb 的变化关系假设上，即人体脑部活动引起的 HbO 和 Hbb 的功能响 [27][28] 根据 fNIRs 的通道设计，可以发现其第 5 和第 6 通道主要位于前额 OFC 区域，与之前研究的情况相符合 [5] [17,22]…”

Section: -unclassified

Emotional State Recognition Based on Functional Near-Infrared Spectroscopy

Jin¹,

Xuejun²,

Jinjin³

et al. 2016

光学学报

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In order to investigate the human emotional state recognition, the functional near-infrared spectroscopy (fNIRs) technique is applied to measure hemodynamic signals of 15 participants who are requested to see six types of pictures, and the participants have to complete 7-point rating scale of valence and arousal after every picture stimulus. The support vector machine (SVM) and support vector machine based recursive feature elimination (SVM-RFE) algorithm are applied to design classifiers. Under different emotional image stimulus, the hemodynamic signals of some participants show significant neural response. With the target classification based on valence, arousal and emotion category, the accuracy is 81%, 78.78% and 68%, respectively. The 5 th and 6 th channels for fNIRs measurement are significantly sensitive to arousal and valence state, and the two channels are located at orbitonfrontal cortex and dorsolateral prefrontal cortex regions. Besides, it is found that the entropy of fNIRs can reflect the variation in emotional state effectively. The results suggest that fNIRs can be used for recognition of human emotional state.

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Section: -unclassified

Emotional State Recognition Based on Functional Near-Infrared Spectroscopy

Jin¹,

Xuejun²,

Jinjin³

et al. 2016

光学学报

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The Processing of EFPI Fiber Gas Detection Signal Based on Hilbert-Huang Transforms Algorithm

Zhang

Xie

et al. 2014

AMM

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In order to obtain spectra from EFPI interference with the noise in the signal spectrum which can get demodulation precision cavity length, HHT method is proposed based gas detection algorithm processing EFPI fiber optic signals. The principle of HHT algorithm is to deal with the noise spectral signal EFPI through empirical mode decomposition (EMD), a group based on the signal obtained their intrinsic mode function (IMF), through to the IMF for Hilbert transform the Hilbert spectrum for time-frequency analysis. Through simulation and numerical analysis, the algorithm to solve the original non-stationary signal interference spectrum , nonlinear shortcomings, through a series of changes to remove noise, which can reduce the difficulty of data processing and increase the accuracy of the gas signal analysis .

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Analysis and Feature Extraction of Laser Micro-Doppler Signatures Based on Hilbert-Huang Transforms

Cited by 2 publications

References 0 publications

Emotional State Recognition Based on Functional Near-Infrared Spectroscopy

Emotional State Recognition Based on Functional Near-Infrared Spectroscopy

The Processing of EFPI Fiber Gas Detection Signal Based on Hilbert-Huang Transforms Algorithm

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