Stress Recognition with EEG Signals Using Explainable Neural Networks and a Genetic Algorithm for Feature Selection

2021 ◽  
pp. 136-143
Author(s):  
Eric Pan ◽  
Jessica Sharmin Rahman
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Feature Selection ◽  
Eeg Signals ◽  
Stress Recognition

Analysis of neural networks and analysis of feature selection with genetic algorithm to discriminate among pollutant gas

2004 ◽  
Vol 103 (1-2) ◽  
pp. 122-128 ◽  
Author(s):  
M. Aleixandre ◽  
I. Sayago ◽  
M.C. Horrillo ◽  
M.J. Fernández ◽  
L. Arés ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Feature Selection

scholarly journals Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

2018 ◽  
Vol 15 (11) ◽  
pp. 2461 ◽  
Author(s):  
Dongkoo Shon ◽  
Kichang Im ◽  
Jeong-Ho Park ◽  
Dong-Sun Lim ◽  
Byungtae Jang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Stress State ◽  
Stress Analysis ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Alpha Asymmetry

In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.

scholarly journals Categories Malware using Neural Networks based on Feature Selection by Genetic algorithm

2015 ◽  
Vol 37 ◽  
pp. 168
Author(s):  
Fatemeh Farahmand ◽  
Seyed Javad Mirabedini
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Feature Selection ◽  
Attack Detection ◽  
Modern World ◽  
Suggested Model ◽  
Notification Rate ◽  
Training Time ◽  
Detection Systems ◽  
Information And Communication

Concurrent with the ever-increasing growth of information and communication technology (ICT) and the dramatic expansion ofcomputer networks, we observe different forms of attacks and intrusions to networks; thus intrusion detection systems (IDSs) are consideredas a vital part of each network connected to internet in the modern world. Neural networks are considered as a popular method used in IDS.Two major problems in these networks, i.e. long training time and inattention to features' domain, have made necessary development and/orimprovement of the model. Feature selection techniques are used in the neural networks in order to develop a new model to speed up theattack detection, to reduce error notification rate and finally to enhance system's efficiency. In this study, for enhancing efficiency of theneural network in detecting intrusions, a genetic algorithm was used for selecting features. The suggested model was examined and assessedon NSL-KDD dataset which is the modified version of the KDD-CUP99. The experimental results indicate that the suggested model is veryefficient in enhancing precision and recall of attack detection and reducing the error notification rate and also is able to offer more accuratedetections in contrast to the basic models

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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