Performance analysis of KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals

2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) ◽

10.1109/wispnet.2016.7566575 ◽

2016 ◽

Cited By ~ 8

Author(s):

M. Manjusha ◽

R. Harikumar

Keyword(s):

Performance Analysis ◽

Eeg Signals ◽

Robust Classification ◽

Knn Classifier

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Robust classification of motor imagery EEG signals using statistical time–domain features

Physiological Measurement ◽

10.1088/0967-3334/34/11/1563 ◽

2013 ◽

Vol 34 (11) ◽

pp. 1563-1579 ◽

Cited By ~ 18

Author(s):

A Khorshidtalab ◽

M J E Salami ◽

M Hamedi

Keyword(s):

Motor Imagery ◽

Time Domain ◽

Eeg Signals ◽

Robust Classification ◽

Statistical Time

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Performance Analysis of ApEn as a Feature Extraction Technique and Time Delay Neural Networks, Multi Layer Perceptron as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG Signals

Advances in Intelligent Systems and Computing - Computational Intelligence, Cyber Security and Computational Models ◽

10.1007/978-981-10-0251-9_10 ◽

2015 ◽

pp. 89-97 ◽

Cited By ~ 3

Author(s):

Sunil Kumar Prabhakar ◽

Harikumar Rajaguru

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Time Delay ◽

Performance Analysis ◽

Extraction Technique ◽

Multi Layer Perceptron ◽

Eeg Signals ◽

Risk Levels ◽

Feature Extraction Technique

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Stationary Common Spatial Patterns: Towards robust classification of non-stationary EEG signals

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2011.5946469 ◽

2011 ◽

Cited By ~ 7

Author(s):

Wojciech Wojcikiewicz ◽

Carmen Vidaurre ◽

Motoaki Kawanabe

Keyword(s):

Spatial Patterns ◽

Eeg Signals ◽

Robust Classification ◽

Common Spatial Patterns

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The Classification of Wink-Based EEG Signals: The Identification on Efficiency of Transfer Learning Models by Means of kNN Classifier

Lecture Notes in Mechanical Engineering - RiTA 2020 ◽

10.1007/978-981-16-4803-8_22 ◽

2021 ◽

pp. 205-213

Author(s):

Jothi Letchumy Mahendra Kumar ◽

Mamunur Rashid ◽

Rabiu Muazu Musa ◽

Mohd Azraai Mohd Razman ◽

Norizam Sulaiman ◽

...

Keyword(s):

Transfer Learning ◽

Learning Models ◽

Eeg Signals ◽

Knn Classifier

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Performance analysis of SVD and K-means clustering for optimization of fuzzy outputs in classification of epilepsy risk level from EEG signals

2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology ◽

10.1109/ecticon.2012.6254317 ◽

2012 ◽

Cited By ~ 1

Author(s):

R. Harikumar ◽

T. Vijayakumar ◽

M.G. Sreejith

Keyword(s):

Performance Analysis ◽

Risk Level ◽

Eeg Signals

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Performance Analysis of Wavelet Transforms and Principal Components as Post Classifier for the Classification of Epilepsy Risk Levels from EEG Signals

Lecture Notes in Electrical Engineering - Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012) ◽

10.1007/978-81-322-0997-3_3 ◽

2013 ◽

pp. 25-35 ◽

Cited By ~ 1

Author(s):

R. Harikumar ◽

T. Vijaykumar ◽

C. Ganesh Babu ◽

M. G. Sreejith

Keyword(s):

Performance Analysis ◽

Principal Components ◽

Wavelet Transforms ◽

Eeg Signals ◽

Risk Levels

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Classification of sleep disorders based on EEG signals by using feature extraction techniques with KNN classifier

2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT) ◽

10.1109/igeht.2017.8093976 ◽

2017 ◽

Cited By ~ 1

Author(s):

Dayanand Vishwanath Dhongade ◽

T.V.K.H. Rao

Keyword(s):

Feature Extraction ◽

Sleep Disorders ◽

Extraction Techniques ◽

Eeg Signals ◽

Knn Classifier

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Performance analysis of fuzzy techniques hierarchical aggregation functions decision trees and Support Vector Machine (SVM)for the classification of epilepsy risk levels from EEG signals

2011 IEEE Recent Advances in Intelligent Computational Systems ◽

10.1109/raics.2011.6069364 ◽

2011 ◽

Author(s):

R. Harikumar ◽

T. Vijaykumar ◽

C. Palanisamy

Keyword(s):

Support Vector Machine ◽

Performance Analysis ◽

Decision Trees ◽

Support Vector ◽

Eeg Signals ◽

Risk Levels ◽

Hierarchical Aggregation ◽

Aggregation Functions ◽

Fuzzy Techniques

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Performance analysis of deep learning CNN in classification of depression EEG signals

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) ◽

10.1109/tencon.2019.8929254 ◽

2019 ◽

Cited By ~ 1

Author(s):

P Sandheep ◽

S Vineeth ◽

Meljo Poulose ◽

D P Subha

Keyword(s):

Deep Learning ◽

Performance Analysis ◽

Eeg Signals

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Performance analysis of SVD and Support Vector Machines for optimization of fuzzy outputs in classification of epilepsy risk level from EEG signals

2011 IEEE Recent Advances in Intelligent Computational Systems ◽

10.1109/raics.2011.6069404 ◽

2011 ◽

Cited By ~ 3

Author(s):

R. Harikumar ◽

T. Vijayakumar ◽

M.G. Sreejith

Keyword(s):

Support Vector Machines ◽

Performance Analysis ◽

Risk Level ◽

Support Vector ◽

Eeg Signals ◽

Vector Machines

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