Improving Efficiency in EEG process using Linear Discriminant Analysis and Support Vector Machine
Lella Kranthi Kumar
Mr. Lella Kranthi Kumar, Assistant Professor, Department of CSE, LBRCE, Mylavaram (Andhra Pradesh), India.
Manuscript received on 30 March 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 27 April 2019 | PP: 910-913 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F11090476S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The advancement in the bio medical examinations with the help of advanced technologies is tremendously increasing. Here we represent a model in which the EEG images of frequency signals can be processed with the help of machine learning techniques and finding their seizure. We mentioned the accuracy to let the readers know how to get the most out of the given EEG images if processed. This also is process in which we process the images by extracting the features of the EEG by the usage of LDA and then changing over to SVM for the latter classification. This can also be useful for calculation of sleep states and neural disorders due to lack of neural activity in the brain. BCI plays a key role in the supply of the images of EEG and its analysis.
Keywords: EEG, Linear Discriminate Analysis, SVM, Classification, Seizure.
Scope of the Article: Machine Learning