Classification of EEG based control using ANN and KNN — A comparison

Poorna, S. S.; Baba, P M V D Sai; Ramya, G Lakshmi; Poreddy, Prasanna; Aashritha, L. S.; Nair, G. J.; Renjith, S.

doi:10.1109/iccic.2016.7919524

Cited by 22 publications

(7 citation statements)

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“…Where Xsj is an object at coordinate sj, ytj is another object at coordinate tj and dst is a distance between them. The advantage of using EU distance metric in k-NN model is the most universal and great for low-dimensional data [50], [51]. Existing work in [43] conducts research on classifying agarwood oil into high and low quality.…”

Section: Distance Metricsmentioning

confidence: 99%

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

Huzir

Mahabob

Amidon

et al. 2022

IJEECS

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Essential <span>oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.</span>

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Section: Distance Metricsmentioning

confidence: 99%

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

Huzir

Mahabob

Amidon

et al. 2022

IJEECS

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“…• Real-time teleoperation of robotic body parts [18][19][20][21][22]; • Controlling and directing a robot [23][24][25][26], drone [27], dashboard of a vehicle [28], or a miniature or semi-automated car [29,30]; • Monitoring and controlling sensors inside of smart houses [31].…”

Section: Autonomous Navigation Of Digital or Mechatronic Devicesmentioning

confidence: 99%

Electroencephalography (EEG) Technology Applications and Available Devices

2020

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The electroencephalography (EEG) sensor has become a prominent sensor in the study of brain activity. Its applications extend from research studies to medical applications. This review paper explores various types of EEG sensors and their applications. This paper is for an audience that comprises engineers, scientists and clinicians who are interested in learning more about the EEG sensors, the various types, their applications and which EEG sensor would suit a specific task. The paper also lists the details of each of the sensors currently available in the market, their technical specs, battery life, and where they have been used and what their limitations are.

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“…(2) Individual classifiers are as independent as possible, otherwise the integration effect is not obvious. Thus we selected KNN [49], LDA [50] and SVM [51] as individual classifiers with different classification algorithms.…”

Section: ) Ensemble Learning-based Classificationmentioning

confidence: 99%

Identity Authentication Using Portable Electroencephalography Signals in Resting States

RongKai

Yan

et al. 2019

IEEE Access

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Biometric identity authentication technology is widely used in the field of information security. As a new biological method, electroencephalography (EEG) is gradually applied in biometric recognition, because scientists believe that practicability and portability are the development direction of EEG identity authentication. This work investigates the feasibility of using resting state EEG signals recorded by single-channel portable device for identity authentication. Single-channel EEG classification are effectively improved by using mixed-method in three feature domains (i.e., time, frequency, and timefrequency), feature selection (Rayleigh quotient) and classifier design (ensemble classifier). We invited 46 subjects to participate in the EEG identity authentication experiment. Experimental results show that the open-eyed resting state is an ideal authentication method, and the average classification accuracy of the authentication algorithm can reach 95.48% in 2 seconds, validating that the new method of processing single-channel EEG signals is especially useful in extrapolating EEG identity authentication to realistic field contexts.

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Classification of EEG based control using ANN and KNN — A comparison

Cited by 22 publications

References 0 publications

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

Electroencephalography (EEG) Technology Applications and Available Devices

Identity Authentication Using Portable Electroencephalography Signals in Resting States

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