The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals

Sabancı, Kadir; Köklü, Murat

doi:10.18201/ijisae.75836

Cited by 48 publications

(29 citation statements)

References 11 publications

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“…In [12], the k-NN classifier obtained the best accuracy in classifying EEG signals to identify the engagement, enjoyment, frustration and difficulty compared to Bayes Network, Naïve Bayes, SVM, Multilayer Perceptron, Random Forest and J48. Besides that, [13] shows that the k-NN also provides better accuracy in eye state classification compared to Multilayer Perceptron Neural Networks.…”

Section: Introductionmentioning

confidence: 94%

“…In k-NN, distance metrics is used to calculate distance between a new samples and existing samples in dataset. The literature is strongly influenced by a commonplace in using the Euclidean distance metric [10][11][12][13] [15][16][17][18]. In fact, we are not aware of any studies with a focus on a performance comparison among various different distance metrics for k-NN.…”

Section: Introductionmentioning

confidence: 99%

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The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

et al. 2017

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Abstract. Most EEG-based motor imagery classification research focuses on the feature extraction phase of machine learning, neglecting the crucial part for accurate classification which is the classification. In contrast, this paper concentrates on the classifier development where it thoroughly studies the performance analysis of k-Nearest Neighbour (k-NN) classifier on EEG data. In the literature, the Euclidean distance metric is routinely applied for EEG data classification. However, no thorough study has been conducted to evaluate the effect of other distance metrics to the classification accuracy. Therefore, this paper studies the effectiveness of five distance metrics of k-NN: Manhattan, Euclidean, Minkowski, Chebychev and Hamming. The experiment shows that the distance computations that provides the highest classification accuracy is the Minkowski distance with 70.08%. Hence, this demonstrates the significant effect of distance metrics to the k-NN accuracy where the Minknowski distance gives higher accuracy compared to the Euclidean. Our result also shows that the accuracy of k-NN is comparable to Support Vector Machine (SVM) with lower complexity for EEG classification.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

et al. 2017

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“…kNN is the simplest controlled learning algorithm among the whole machine learning algorithms [14]. It doesn't use the training data points to do any generalization so it is also called a lazy algorithm.…”

Section: K-nearest Neighbor Algorithmmentioning

confidence: 99%

“…These techniques usually need training and generalization procedure to make best estimation. On the other hand, k-nearest neighbor (kNN) doesn't need to use the training data points to do any generalization so it can be easily used for many categorizations or classification [14][15][16]. Although kNN is the simplest controlled learning algorithm, it hasn't been used to estimate operating frequency of the antennas so far.…”

Section: Introductionmentioning

confidence: 99%

Operating Frequency Estimation of Slot Antenna by Using Adapted kNN Algorithm

Yiğit

2018

ijisae

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In this study ultra-high frequency slot antenna's operating frequency is estimated by using adapted k-nearest neighbor (kNN) algorithm. kNN doesn't use the training data points to do any generalization and it can be usually used for many classification. However, kNN can be adapted to estimate slot antenna's operating frequency by assessing the best k-nearest value. To find the optimal k for operating frequency estimation, 96 slot antennas with seven antenna parameters are simulated with respect to the operating frequency by using a computational electromagnetic software. Antenna parameters includes the patch dimensions, height and relative permittivity of the substrate. The simulated 81 antennas are used to construct feature data pool and the residual 15 antennas are used to test kNN algorithm. The performance of the kNN is evaluated by comparing the output of operating frequency to the simulated one. Then the proposed model is corroborated with simulated antennas and validating with prototyped antenna data. The results shows that the kNN based model simply and fast computes the operating frequency of the slot antennas much close to real one without performing any simulations or measurement.

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“…Además, Rsler y Suendermann [26] generan un corpus con cerca de 15, 000 muestras y someten los datos a 42 clasificadores auxiliándose de la plataforma WEKA [29]. [27,28,30,31] utilizan el mismo corpus( [26]) para aplicar otros enfoques de clasificación a los ya reportados. Fig.…”

Section: Electroencefalogramasunclassified

Técnicas de aprendizaje automático aplicadas a electroencefalogramas

Cruz¹,

Ramírez²,

Trujillo-Romero³

2016

RCS

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Resumen. Los electroencefalogramas(EEG) almacenan la actividad cerebral en un instante de tiempo determinado. Actualmente los EEG son utilizados en disciplinas médicas, científicas y tecnológicas. Debido a esto, lograr interpretar y clasificar de forma precisa estas señales es un problema abierto en elárea de machine learning. En esta investigación se trabaja con un conjunto de datos que representan señales de EEG, en las cuales se encuentran codificados los estados de los ojos (abiertos o cerrados). Este conjunto ha sido analizado por diversos autores, lo que se propone en esta investigación es tratar de mejorar el desempeño de los métodos ya explorados. Por lo anterior, se explora mediante una búsqueda greedy los valores de los parámetros que ofrezcan un mayor rendimiento al utilizar SVM y RNA. También se aplican técnicas de reducción mediante selección de prototipos y extracción de características al conjunto de señales. El objetivo es tratar de mejorar la estructura de los datos mediante una representación más compacta, para posteriormente comprobar si se logra mejorar el proceso de discriminación.Palabras clave: EEG, SVM, redes neuronales artificiales, extracción de características, selección de prototipos, optimización, predición del los estados de los ojos.

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The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals

Cited by 48 publications

References 11 publications

The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

Operating Frequency Estimation of Slot Antenna by Using Adapted kNN Algorithm

Técnicas de aprendizaje automático aplicadas a electroencefalogramas

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