EEG Signal Processing Based on Genetic Algorithm for Extracting Mixed Features

Abstract

In order to improve the classification of motor imagery EEG accuracy, this paper proposes a method based on Genetic Algorithm (GA) EEG signal classification method to extract mixed characteristics. This method uses wavelet analysis and Hilbert–Huang Transform (HHT) to analyze EEG signals and optimizes the characteristics through Common Spatial Patterns (CSP). Finally, the 14 sub features are optimized by GA, and the weights and data credibility of different sub features are obtained. The experiment was tested with 2003BCI competition data and the EEG signal collected by the laboratory. The accuracy rate of competition data was increased from about 75% before weighting to more than 80% after weighting, and the laboratory data increased from about 65% before weighting to about 75% after weighting. Experimental results show that this method can effectively improve the classification accuracy of EEG signals, and the most useful EEG signals can be extracted from large amounts of data for feature extraction and classification. Finally, the online test is carried out to further verify the feasibility of the method.

Recognition, Processing, and Detection of Sensor Fault Signal Based on Genetic Algorithm

“…Its main feature is to directly calculate and process the optimization target. As a random search and iterative optimization algorithm, the genetic algorithm has potential parallelism and global optimization ability with no differentiable and continuous limitation on objective function [2]. Zhang and others proposed the Wireless Sensor and Actuator Network (WSAN), each sensor node in the system senses and collects the changes of the monitoring object, and transmits the information to the actuator for real-time response [3].…”

“…It is almost the simplest smooth convex function, the maximum value f ð maxÞ = J f ð5:5Þ = 10, and there is only one maximum coordinate point (2) Step function:…”

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

“…Finally, we would like to suggest some of the possible scopes, shortly for researcher and practitioner as a brainstorming concept: reducing the reaction time and maximizing VM's resource allocation considering the QoS factor; improving the load stability in WSN using RCNN learning; SVM-PSO based community Forensics and RNN techniques for Intrusion Detection. Feature selection from natural algorithm Koroniotis et al [133] Quality of service NF PSO and DL Enhance NF Al hawaitat et al [134] WS PNS PSO Jamming attack Shi et al [135] Anomaly detection P ADAID 1 Presented unsupervised clustering Usman et al [96] VM allocation VR EFPA 2 Energy-oriented allocation Singh et al [103] VM migration VR HBGA 3 Energy reduction Naik et al [130] VM allocation VR Fruit fly Reduce host migration Meng & Pan [136] Optimization VR FFOA solve MKP 4 Mosa & Paton [126] VM placement VR GA Reduce response time & maximize resources utilization Duan et al [137] Information leakage P DL Protect server Festag & Spreckelsen [138] Data leakage P DL Detection of protected health information Chari et al [125] Quality of service IA DL Generate password via cognitive information Li et al [139] Signal processing IA GA Feature extraction via EEG signal Saini & Kansal [127] WSN ACS SI Reduce energy consumption and increase network life time Chen et al [140] Biometric identification IA CNN Proposed GSLT-CNN using human brain EEG Cao & Fang [141] Multilayer defense scenario ACS SI Found proficient IPSO elucidating extensive WTA problem Aliyu et al [124] Resource allocation ACS Ant colony Illustrated faster convergence optimize makespan time Poonia [142] VAN ACS SI Found significant difference in VANET routing protocol and Swarm based protocol Verma et al [ [129] Feature extraction ID GA Reduce features to classify network packet Tan et al [148] Real time network attack intrusion ID NN Able to detect in network precisely…”

Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm

“…Therefore, when extracting text features, the feature words in the same text can be put into a feature vector representing the text, so as to avoid ignoring the connection between feature items [15][16]. On this basis, this paper proposes a text feature vector on the basic of X 2 statistics, which can not only preserve the correlation between text features, but also distinguish the correlation between features and classes; and uses this vector as the initial population, through multiple rounds of genetic vectors are obtained to improve classification accuracy; through the coordination of crossover operation and mutation operation, global search can be realized and local minima can be avoided [17][18]; according to the characteristics of feature extraction, the fitness function and intersection rules are designed to solve the problem of inappropriate processing of low-frequency words in statistical analysis [19][20]. The flow chart of feature extraction on the basic of GA is shown in Figure 2: The figure 3 shows the average fitness of the population optimized by the GA has reached above 0.935, and these data show that the fitness of individuals in the population is better, and the effect of evolution is better.…”