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Your search keyword '"Hou, Huirang"' showing total 13 results
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13 results on '"Hou, Huirang"'

1. An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction

Author

Sun, Biao, Wei, Zhigang, Liang, Pei, and Hou, Huirang

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific frequency bands for classification performance is important. Moreover, due to the large inter-subject variability of the EEG signals, extracting frequency bands with subject-specific information rather than general information is crucial. Considering these, the focus of this letter is to classify the olfactory EEG signals by exploiting the spectral-domain information of specific frequency bands. In this letter, we present an olfactory EEG signal classification network based on frequency band feature extraction. A frequency band generator is first designed to extract frequency bands via the sliding window technique. Then, a frequency band attention mechanism is proposed to optimize frequency bands for a specific subject adaptively. Last, a convolutional neural network (CNN) is constructed to extract the spatio-spectral information and predict the EEG category. Comparison experiment results reveal that the proposed method outperforms a series of baseline methods in terms of both classification quality and inter-subject robustness. Ablation experiment results demonstrate the effectiveness of each component of the proposed method.

Published
2022

11. Research and realization of ultrasonic gas flow rate measurement based on ultrasonic exponential model

Author

Zhang Tao, Hou Huirang, and Zheng Dandan

Subjects
Accuracy and precision, Acoustics and Ultrasonics, Noise (signal processing), System of measurement, Models, Theoretical, 01 natural sciences, Local convergence, Exponential function, 010309 optics, Control theory, Software Design, 0103 physical sciences, Personal computer, Calibration, Ultrasonic sensor, Ultrasonics, Gases, 010301 acoustics, Energy (signal processing), Mathematics
Abstract

For ultrasonic gas flow rate measurement based on ultrasonic exponential model, when the noise frequency is close to that of the desired signals (called similar-frequency noise) or the received signal amplitude is small and unstable at big flow rate, local convergence of the algorithm genetic-ant colony optimization-3cycles may appear, and measurement accuracy may be affected. Therefore, an improved method energy genetic-ant colony optimization-3cycles (EGACO-3cycles) is proposed to solve this problem. By judging the maximum energy position of signal, the initial parameter range of exponential model can be narrowed and then the local convergence can be avoided. Moreover, a DN100 flow rate measurement system with EGACO-3cycles method is established based on NI PCI-6110 and personal computer. A series of experiments are carried out for testing the new method and the measurement system. It is shown that local convergence doesn't appear with EGACO-3cycles method when similar-frequency noises exist and flow rate is big. Then correct time of flight can be obtained. Furthermore, through flow calibration on this system, the measurement range ratio is achieved 500:1, and the measurement accuracy is 0.5% with a low transition velocity 0.3 m/s.

Published
2015

13. [Olfactory electroencephalogram signal recognition based on wavelet energy moment].

Author

Zhai W, Zhang X, Hou H, and Meng Q

Subjects
Electroencephalography, Entropy, Humans, Support Vector Machine, Recognition, Psychology, Wavelet Analysis
Abstract

Studying the ability of the brain to recognize different odors is of great significance in the assessment and diagnosis of olfactory dysfunction. The wavelet energy moment (WEM) was proposed as a feature of olfactory electroencephalogram (EEG) signal and used for odor classification. Firstly, the olfactory evoked EEG data of 13 odors were collected by an experiment. Secondly, the WEM was extracted from olfactory evoked EEG data as the signal feature, and the power spectrum density (PSD), approximate entropy, sample entropy and wavelet entropy were used as the contrast features. Finally, k -nearest neighbor ( k -NN), support vector machine (SVM), random forest (RF) and decision tree classifier were used to identify different odors. The results showed that using the above four classifiers, the classification accuracy of WEM feature was higher than other features, and the k -NN classifier combined with WEM feature had the highest classification accuracy (91.07%). This paper further explored the characteristics of different EEG frequency bands, and found that most of the classification accuracy based on the features of γ band was better than that of the full band and other bands, among which the WEM feature of the γ band combined with the k -NN classifier had the highest classification accuracy (93.89 %). The research results of this paper could provide a new objective basis for the evaluation of olfactory function. On the other hand, it could also provide new ideas for the study of olfactory-induced emotions.

Published
2020
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