scholarly journals Algorithms for Classification of Signals Derived From Human Brain

2021 ◽  
Vol 15 ◽  
pp. 1521-1526
Author(s):  
Georgi P. Dimitrov ◽  
Galina Panayotova ◽  
Boyan Jekov ◽  
Pavel Petrov ◽  
Iva Kostadinova ◽  
...  
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Signal Acquisition ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Non Invasive ◽  
Basic Parameters ◽  
Linear Svm

Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.

scholarly journals Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Maulana Angga Pribadi
Keyword(s):  
Signal Analysis ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Epilepsy Classification ◽  
Non Stationary Signal ◽  
The Brain

Epileps is a disorder of the contents of the nervous system of the human brain resulting in the presence of abnormal activity that is the excessive activity of neuron cells in the brain. In Indonesia there are more than 1,400,000 cases of Epilepsy each year with 70,000 additional cases each year. About 4050% occurs in children. A widely used method for assessing brain activity is through a sephalogram (EEG) Electrone signal. The Epilepsy classification system is built with extraction and identifikas stages. Wavelet exctraction is suitable for non-stationary signal analysis such as EEG signals. Wavelet tranformation can extract signal components only at the required frequency. So that it can also reduce the amount of data but without losing meaningful information. But to make it work and can be used on a system needs to be done classification in order to be able to distinguish between commands from each other. So it is used K-Nearest Neighbour (K-NN) classification method so that the signal that has been eliminated buzz can be directly entered into the classification to determine the correct wrongness of a data. In this study obtained the results of data accuracy value that K = 1 has the largest percent of 100% and the smallest percent is found in K = 7 and K = 11 namely 14.2% and 18.2% it is caused by the presence of classes that do not match the test data so as to reduce the percentage of accuracy in the K.

scholarly journals Classification of EEG Signals using Nonlinear Features and Preprocessing Techniques

2021 ◽  
Vol 10 (5) ◽  
pp. 297-301
Author(s):  
Saneesh Cleatus T ◽  
Dr. Thungamani M
Keyword(s):  
Brain Tumor ◽  
Nearest Neighbor ◽  
Processing Technique ◽  
Support Vector ◽  
Statistical Features ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Nonlinear Features

In this paper we study the effect of nonlinear preprocessing techniques in the classification of electroencephalogram (EEG) signals. These methods are used for classifying the EEG signals captured from epileptic seizure activity and brain tumor category. For the first category, preprocessing is carried out using elliptical filters, and statistical features such as Shannon entropy, mean, standard deviation, skewness and band power. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used for the classification. For the brain tumor EEG signals, empirical mode decomposition is used as a pre-processing technique along with standard statistical features for the classification of normal and abnormal EEG signals. For epileptic signals we have achieved an average accuracy of 94% for a three-class classification and for brain tumor signals we have achieved a classification accuracy of 98% considering it as a two class problem.

scholarly journals Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

2020 ◽  
Vol 8 (4) ◽  
pp. 270-275
Author(s):  
Deden Istiawan
Keyword(s):  
Information System ◽  
Random Forest ◽  
Satellite Images ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Random Forest Algorithm ◽  
K Nearest Neighbor

Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.

scholarly journals Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF)

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Neneng Nur Amalina ◽  
Kurniawan Nur Ramadhani ◽  
Febryanti Sthevanie
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Performance Comparison ◽  
Cellular Heterogeneity ◽  
Experimental Result ◽  
Cell Type ◽  
K Nearest Neighbor ◽  
Nuclei Detection ◽  
High Degree

Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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