An Incremental Feature Reordering (IFR) Algorithm to Classify Eye State Identification Using EEG

Sahu, Mridu; Nagwani, Naresh Kumar; Verma, Shrish; Shirke, Saransh

doi:10.1007/978-81-322-2250-7_80

Cited by 12 publications

(5 citation statements)

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“…This set declared as MND set, removing of this electrodes from EEG corpus sufficiently decrease the space and time requirement to built the classification model. The accuracy towards the classification changed very less and this analysis outcome shown in table [3], figure [6]. The Confusion matrix shown in figure [5] and ROC curve shown in figure [7], evaluate the classifier performance here the classifier is Instance based classifier (K*), the classification accuracy is computed and it is mapped in table [3].…”

Section: Proposed Methodology For Mnd Setmentioning

confidence: 99%

“…The present work is performed with EEG electrode data having 16 electrodes and 14892 instances [4,5]. This uses the instance based classifier (K*), because based on statistic of data and nature of data spread over the corpus found it is best among other classifier the result of this present in literature [6,7], [28], [33], [38]. Method selects either one electrode, two electrode or three electrodes based on how much search space the corpus wants to reduce.…”

Section: Introductionmentioning

confidence: 99%

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Finding Non Dominant Electrodes Placed in Electroencephalography (EEG) for Eye State Classification using Rule Mining

Sahu¹,

Nagwani²,

ShrishVerma³

2016

ijacsa

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Abstract-Electroencephalography is a measure of brain activity by wave analysis; it consist number of electrodes. Finding most non-dominant electrode positions in Eye state classification is important task for classification. The proposed work is identifying which electrodes are less responsible for classification. This is a feature selection step required for optimal EEG channel selection. Feature selection is a mechanism for subset selection of input features, in this work input features are EEG Electrodes. Most Non Dominant (MND), gives irrelevant input electrodes in eye state classification and thus it, reduces computation cost. MND set creation completed using different stages. Stages includes, first extreme value removal from electroencephalogram (EEG) corpus for data cleaning purpose. Then next step is attribute selection, this is a preprocessing step because it is completed before classification step. MND set gives electrodes they are less responsible for classification and if any EEG electrode corpus wants to remove feature present in this set, then time and space required to build the classification model is (20%) less than as compare to all electrodes for the same, and accuracy of classification not very much affected. The proposed article uses different attribute evaluation algorithm with Ranker Search Method.

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Section: Proposed Methodology For Mnd Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finding Non Dominant Electrodes Placed in Electroencephalography (EEG) for Eye State Classification using Rule Mining

Sahu¹,

Nagwani²,

ShrishVerma³

2016

ijacsa

Self Cite

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“…Results show that IAL can efficiently cope with time series classification problems with proper feature extraction and feature ordering. (Sahu et al 2015) investigated the EEG system's eye state identification by finding feature subset selection named Incremental Feature Reordering (IFR). It provides the most non-dominant feature (MND) for Electroencephalography (EEG) signal corpus and creates a reorder set.…”

Section: Literature Reviewmentioning

confidence: 99%

High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

Bataineh

Jarrah

2022

International Journal of Applied Metaheuristic Computing

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EEG analysis aims to help scientists better understand the brain, help physicians diagnose and treatment choices of the brain-computer interface. Artificial neural networks are among the most effective learning algorithms to perform computing tasks similar to biological neurons in the human brain. In some problems, the neural network model's performance might significantly degrade and overfit due to some irrelevant features that negatively influence the model performance. Swarm optimization algorithms are robust techniques that can be implemented to find optimal solutions to such problems. In this paper, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms are applied for the feature selection and the training of a Feed-forward Neural Network (FFNN). The performance of the FFNN in terms of test accuracy, precision, recall, and F1_score is investigated. Furthermore, this research has implemented other five machine learning algorithms for the purpose of comparison. Experimental results prove that the neural network model outperforms all other algorithms via GWO.

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“…They found that the removal of MND gives optimal subset feature and it increases the classifier accuracy and efficiency [16].…”

Section: A the Classification Studies On Eeg Eye State Medical Datasetmentioning

confidence: 99%

Performance of Classification Techniques on Medical Datasets

Tutuncu¹,

Köklü²

2015

Third International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015

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An Incremental Feature Reordering (IFR) Algorithm to Classify Eye State Identification Using EEG

Cited by 12 publications

References 10 publications

Finding Non Dominant Electrodes Placed in Electroencephalography (EEG) for Eye State Classification using Rule Mining

Finding Non Dominant Electrodes Placed in Electroencephalography (EEG) for Eye State Classification using Rule Mining

High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

Performance of Classification Techniques on Medical Datasets

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