Performance Analysis of ApEn as a Feature Extraction Technique and Time Delay Neural Networks, Multi Layer Perceptron as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG Signals

2015 ◽  
pp. 89-97 ◽  
Author(s):  
Sunil Kumar Prabhakar ◽  
Harikumar Rajaguru
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Time Delay ◽  
Performance Analysis ◽  
Extraction Technique ◽  
Multi Layer Perceptron ◽  
Eeg Signals ◽  
Risk Levels ◽  
Feature Extraction Technique

scholarly journals Recognition of Rotating Images Using an Automatic Feature Extraction Technique and Neural Networks

1997 ◽  
Vol 08 (02) ◽  
pp. 201-207 ◽  
Author(s):  
Brijesh Verma
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Large Scale ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Extraction Technique ◽  
Global Features ◽  
Automatic Feature Extraction ◽  
Large Size ◽  
Feature Extraction Technique

This paper presents a new automatic feature extraction technique and a neural network based classification method for recognition of rotating images. The image processing technique extracts global features of an image and converts a large size image into a one-dimensional small vector. A special advantage of the proposed technique is that the extracted features are the same even if the original image is rotated with rotation angles from 5 to 355 or rotated and a little bit distorted. The proposed approach technique is based on simple co-ordinate geometry, fuzzy sets and neural networks. The proposed approach is very easy in implementation and its has been developed in C++ on a Sun workstation. The experimental results have demonstrated that the proposed approach performs successfully on a variety of small as well as large scale rotated and distorted images.

Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks

1998 ◽  
Vol 212 (2) ◽  
pp. 87-97 ◽  
Author(s):  
T T Le ◽  
J Watton ◽  
D T Pham
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Power Systems ◽  
Linear Prediction ◽  
Large Data ◽  
Fault Classification ◽  
Extraction Technique ◽  
Dynamic Feature ◽  
Fluid Power Systems ◽  
Feature Extraction Technique

Multilayer perceptron (MLP) type neural networks and dynamic feature extraction techniques, namely linear prediction coding (LPC) and LPC cepstrum, are used to classify leakage type and to predict leakage flowrate magnitude in an electrohydraulic cylinder drive. Both single-leakage and multiple-leakage type faults are considered. A novel feature is that only pressure transient responses are employed as information. In addition, the feature extraction technique used to detect faults can result in a large data dimensionality reduction. The performance of two MLP models, namely serial and parallel, are studied to reflect the importance of the way data are presented to the MLP.

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%.

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