scholarly journals EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 221
Author(s):  
Emigdio Z-Flores ◽  
Leonardo Trujillo ◽  
Pierrick Legrand ◽  
Frédérique Faïta-Aïnseba
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Predictive Accuracy ◽  
Mental States ◽  
Classification Systems ◽  
Linear Transformations ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Extraction Algorithm

The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers, significantly improving upon previously published results on the same real-world dataset.

scholarly journals Detection and classification of disturbances in the islanded micro-grid by using wavelet transformation and feature extraction algorithm

2019 ◽  
Vol 2019 (18) ◽  
pp. 5284-5286 ◽  
Author(s):  
Yunqi Wang ◽  
Ahmed Raza ◽  
Faisal Parvez Mohammed ◽  
Jayashri Ravishankar ◽  
Toan Phung
Keyword(s):  
Feature Extraction ◽  
Wavelet Transformation ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Micro Grid

A Robust Feature Extraction Algorithm for the Classification of Acoustic Targets in Wild Environments

2015 ◽  
Vol 34 (7) ◽  
pp. 2395-2406 ◽  
Author(s):  
Jingchang Huang ◽  
Shiliang Xiao ◽  
Qianwei Zhou ◽  
Feng Guo ◽  
Xing You ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Robust Feature Extraction

scholarly journals Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

2019 ◽  
Vol 8 (9) ◽  
pp. 1689-1694
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Non Invasive ◽  
Band Limited ◽  
Ca Sensitivity

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

scholarly journals Automatically extracting features for face classification using multi-objective genetic programming

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Classification Accuracy ◽  
Multi Objective ◽  
Face Classification ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

© 2020 Owner/Author. This paper proposes a new multi-objective feature extraction algorithm using genetic programming (GP) for face classification. The new multi-objective GP-based feature extraction algorithm with the idea of non-dominated sorting, which aims to maximise the objective of the classification accuracy and minimise the objective of the number of extracted features. The results show that the proposed algorithm achieves significantly better performance than the baseline methods on two different face classification datasets.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

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