scholarly journals Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
R. Salazar-Varas ◽  
Roberto A. Vazquez
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
High Variability ◽  
Training Data ◽  
Extraction Technique ◽  
Eeg Signals ◽  
Data Set ◽  
Cutoff Frequencies ◽  
Public Data ◽  
Feature Extraction Technique

In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability. Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected. Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day. The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications. Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated. This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks. By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model. Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification. The increase rate is approximately of 17%.

scholarly journals An Online Degradation Feature Extraction Technique for Shore Bridge Gearbox Based on Morphological Fractal Dimension and Sliding Window Weibull Fitting

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Dejian Sun ◽  
Bing Wang ◽  
Xiong Hu ◽  
Wei Wang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Energy Spectrum ◽  
Scale Parameter ◽  
Sliding Window ◽  
Performance Degradation ◽  
Extraction Technique ◽  
Vibration Energy ◽  
Fractal Curve ◽  
Feature Extraction Technique

Shore bridge and other port cranes have some working condition characters including high speed, heavy load, and large impact. In order to solve the degradation feature extraction issue of hoisting mechanism gearbox, an online degradation feature extraction technique based on morphological fractal dimension and sliding window Weibull fitting is proposed. Firstly, taking the vibration energy spectrum collecting from the gearbox as the online data source, the fractal dimension of the vibration energy spectrum during an analysis period is calculated and a fractal evolution curve is obtained. A three-parameter Weibull fitting on the fractal curve within a sliding window after setting the window’s width and step size is performed. The scale parameter of the Weibull fitting model is introduced as the performance degradation feature. The effectiveness of the technique is verified by the full-life vibration data of hoisting gearbox from Shanghai Port Group. The results show that the morphological fractal dimension is able to describe the fractal complexity of the vibration energy spectrum. The scale parameter of Weibull distribution is able to reflect the performance degradation trend of fractal curve smoothly, which lays a theoretical foundation for further solving the problem of online health assessment.

scholarly journals Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

2020 ◽  
Vol 19 (2) ◽  
pp. 1-11
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Shuai Zhang ◽  
Abd El Kader Isselmou ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Technique ◽  
Support Vector ◽  
Single Channels ◽  
Eeg Signals ◽  
Feed Forward Neural Network ◽  
Hybrid Features ◽  
Time Frequency ◽  
Sensitivity Specificity ◽  
Feature Extraction Technique

These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.

S-DCTNet: Security-oriented biometric feature extraction technique

2021 ◽  
Author(s):  
Mohamed Yassine Haouam ◽  
Abdallah Meraoumia ◽  
Lakhdar Laimeche ◽  
Issam Bendib
Keyword(s):  
Feature Extraction ◽  
Extraction Technique ◽  
Biometric Feature ◽  
Feature Extraction Technique

A hybrid WD-EEMD sEMG feature extraction technique for lower limb activity recognition

2021 ◽  
pp. 1-1
Author(s):  
Ankit Vijayvargiya ◽  
Vishu Gupta ◽  
Rajesh Kumar ◽  
Nilanjan Dey ◽  
Joao Manuel R. S. Tavares
Keyword(s):  
Feature Extraction ◽  
Activity Recognition ◽  
Lower Limb ◽  
Extraction Technique ◽  
Feature Extraction Technique
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