Feature Selection Using Binary Simulated Kalman Filter for Peak Classification of EEG Signals

2018 ◽  
Author(s):  
Badaruddin Muhammad ◽  
Mohd Falfazli Mat Jusof ◽  
Mohd Ibrahim Shapiai ◽  
Asrul Adam ◽  
Zulkifli Md Yusof ◽  
...  
Keyword(s):  
Feature Selection ◽  
Kalman Filter ◽  
Eeg Signals

scholarly journals Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals

SpringerPlus ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Asrul Adam ◽  
Zuwairie Ibrahim ◽  
Norrima Mokhtar ◽  
Mohd Ibrahim Shapiai ◽  
Marizan Mubin ◽  
...  
Keyword(s):  
Feature Selection ◽  
Kalman Filter ◽  
Eeg Signals

scholarly journals An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

2021 ◽  
Vol 11 (15) ◽  
pp. 6983
Author(s):  
Maritza Mera-Gaona ◽  
Diego M. López ◽  
Rubiel Vargas-Canas
Keyword(s):  
Feature Selection ◽  
Sensitivity And Specificity ◽  
Eeg Signals ◽  
Specificity And Sensitivity ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
The Stability ◽  
Selection Algorithms ◽  
Epileptiform Events

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.

Genetic Algorithms for Feature Selection for Brain–Computer Interface

2015 ◽  
Vol 29 (05) ◽  
pp. 1559008 ◽  
Author(s):  
Izabela Rejer
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Crucial Problem ◽  
Left And Right ◽  
Selection For ◽  
First Time

The crucial problem that has to be solved when designing an effective brain–computer interface (BCI) is: how to reduce the huge space of features extracted from raw electroencephalography (EEG) signals. One of the strategies for feature selection that is often applied by BCI researchers is based on genetic algorithms (GAs). The two types of GAs that are most commonly used in BCI research are the classic algorithm and the Culling algorithm. This paper presents both algorithms and their application for selecting features crucial for the correct classification of EEG signals recorded during imagery movements of the left and right hand. The results returned by both algorithms are compared to those returned by an algorithm with aggressive mutation and an algorithm with melting individuals, both of which have been proposed by the author of this paper. While the aggressive mutation algorithm has been published previously, the melting individuals algorithm is presented here for the first time.

scholarly journals A Study of the Classification of Motor Imagery Signals using Machine Learning Tools

2021 ◽  
Author(s):  
Anam Hashmi ◽  
Bilal Alam Khan ◽  
Omar Farooq
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Random Forest ◽  
Random Forest Algorithm ◽  
Eeg Signals ◽  
Relaxation State ◽  
Wavelet Transform Analysis ◽  
Imagined Movement

In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

scholarly journals Feature selection with Lasso for classification of ischemic strokes based on EEG signals

2020 ◽  
Vol 1528 ◽  
pp. 012029
Author(s):  
Hendra Angga Yuwono ◽  
Sastra Kusuma Wijaya ◽  
Prawito Prajitno
Keyword(s):  
Feature Selection ◽  
Eeg Signals
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