scholarly journals An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhongliang Yu ◽  
Lili Li ◽  
Wenwei Zhang ◽  
Hangyuan Lv ◽  
Yun Liu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sleep Deprivation ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Mental Fatigue ◽  
Connectivity Analysis ◽  
Signal To Noise ◽  
Denoising Autoencoder ◽  
Feature Extraction Method ◽  
Noise Ratio

Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis.

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

Enhancing the Signal-to-Noise Ratio of X-ray Diffraction Profiles by Smoothed Principal Component Analysis

2005 ◽  
Vol 77 (20) ◽  
pp. 6563-6570 ◽  
Author(s):  
Zeng Ping Chen ◽  
Julian Morris ◽  
Elaine Martin ◽  
Robert B. Hammond ◽  
Xiaojun Lai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray Diffraction ◽  
Signal To Noise ◽  
X Ray ◽  
Noise Ratio

scholarly journals Pemampatan Citra Pas Foto dengan Menggunakan Algoritma Kompresi Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA)

2018 ◽  
Vol 17 (1) ◽  
pp. 102
Author(s):  
M. Azman Maricar ◽  
Oka Widyantara
Keyword(s):  
Principal Component Analysis ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Penelitian ini bertujuan untuk membandingkan hasil kompresi dari algoritma Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA) terhadap citra pas foto, guna menemukan hasil terbaik dari hasil citra kompresi yang kualitas hasilnya tidak berbeda jauh dengan citra aslinya. Alat ukur yang digunakan adalah Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR). Hasil yang diperoleh dalam penelitian ini adalah rata-rata MSE dan PNSR algoritma PCA dapat dikatakan tinggi jika dibandingkan dengan algoritma JPEG. Namun dari segi kualitas citra yang dihasilkan tidak jauh berbeda dengan algoritma JPEG.Dapat dikatakan bahwa algoritma JPEG mampu menghasilkan citra yang lebih baik dibandingkan algoritma PCA. Namun, algoritma PCA tidaklah buruk untuk dijadikan alternatif dalam kompresi citra pas foto.

scholarly journals Improvement of Fast Kurtogram Combined with PCA for Multiple Weak Fault Features Extraction

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1059
Author(s):  
Yongxing Song ◽  
Jingting Liu ◽  
Linhua Zhang ◽  
Dazhuan Wu
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Rotating Machinery ◽  
Features Extraction ◽  
Signal To Noise ◽  
Fault Features ◽  
Weak Fault ◽  
Noise Ratio

Demodulation plays an important role in fault feature extraction for rotating machinery. The fast kurtogram method was proved to be effective for rotating machinery demodulation. However, the demodulation effectiveness of fast kurtogram was poor for multiple fault features extraction under low signal-to-noise ratio. In this paper, an improved method of fast kurtogram, called P-kurtogram, is presented. The proposed method extracted the multiple weak fault features from multiple envelope signals-based principal component analysis. Compared with extracting features from one envelope signal of fast kurtogram, P-kurtogram showed a better demodulation performance for multiple faults. Combined with principal component analysis method, the proposed method also showed a good performance under low signal-to-noise ratio(SNR). By simulation analysis, the P-kurtogram method showed good performance for multiple modulation features extraction and robust performance in demodulation under low SNR. Then, the proposed method was demonstrated by applications of bearing faults detection and propeller detection. The results verified that the P-kurtogram has a better demodulation performance than fast kurtogram for multiple weak fault features extraction, especially under low signal-to-noise ratio. The proposed method provides a reliable basis for multiple weak fault features extraction of rotating machinery.

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