Author : Sharanya A R 1
Date of Publication :7th May 2016
Abstract: Electroencephalogram (EEG) signals are electrical signals that are recorded from the scalp. They can be used to carry out the analysis and identification of brain disorders. EEG signal are non-stationary. Feature extraction of EEG always remains as a major because of the non stationary behavior along with their accurate classification. Support vector machines are supervised learning models used for classification & regression analysis. LDA classifiers are also used for classification. The main objective of the proposed work is to identify the seizures in the EEG waves. Real time databases are collected and filtered to remove the noise. Each EEG channel is normalized & trained using Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers to locate the seizures. Their results are compared based on their performance.
Reference :
-
[1] Shiliang Sun and Jin Zhou “A Review of Adaptive Feature Extraction and Classification Methods for EEGBased Brain-Computer Interfaces “2014 International Joint Conference on Neural Networks (IJCNN) July 6-11, 2014, Beijing, China.
[2] Kyong Ho Lee, Student Member, IEEE, and Naveen Verma, Member, IEEE “A Low Power Processor with Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals” IEEE Journal of Solid-State Circuits, Vol. 48, No. 7, July 2013.
[3] KavitaMahajan, M. R. Vargantwar, Sangita M. Rajput “Classification of EEG using PCA, ICA and Neural Network” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-1, October 2011.
[4] Pradeep D. Prasad, S. VishakaDatta, and KaushikMajumdar “Enhanced Phase and Amplitude Synchronization Toward Focal Seizure Offset” Clinical EEG and Neuroscience 44(1) 16-24 EEG and Clinical Neuroscience Society (ECNS) 2013.
[5] AsifIshfaque. AhsanJavedAwan, Nasir Rashidand JavaidIqbal“ Evaluation of ANN, LDA and Decision Trees for EEG Based Brain Computer Interface” Department of Mechatronics Engineering National University of Sciences and Technology Islamabad, Pakistan.
[6] Mohamed Shakir, AamirSaeed Malik, NidalKamel ,UvaisQidwai “Detection of Partial Seizure: An Application of Fuzzy Rule System for Wearable Ambulatory Systems”.
[7] Pradeep D. Prasad, Member, IEEE, Harsha N. Halahalli, John P. John, and Kaushik K. Majumdar, Senior Member, IEEE “Single-Trial EEG Classification Using Logistic Regression Based on Ensemble Synchronization” IEEE Journal Of Biomedical And Health Informatics, Vol. 18, No. 3, May 2014.
[8] Turkey N Alotaiby, Saleh A Alshebeili, Tariq Alshawi, Ishtiaq Ahmad and Fathi E Abd El-Samie “EEG seizure detection and prediction algorithms: a survey” Alotaiby et al. EURASIP Journal on Advances in Signal Processing 2014, 2014:183.
[9] Sarah N. Abdulkader ,AymanAtia, Mostafa-Sami M. Mostafa “Brain computer interfacing: Applications and challenges” 1110-8665 _ 2015 Production and hosting by Elsevier.
[10] Christopher J.C. Burges “A Tutorial on Support Vector Machines for Pattern Recognition” Kluwer Academic Publishers, Boston. Manufactured in the Netherlands