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Hybrid support vector machine for classification of EEG signals


Article Information

Title: Hybrid support vector machine for classification of EEG signals

Authors: Mohammad Zaini, Ali Omar

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2016

Volume: 11

Issue: 19

Language: English

Categories

Abstract

Reading EEG signals manually is a very difficult and time-consuming task. In many situations, we like to get the results in a very short amount of time (e.g. monitoring seizure patients). In other cases, we like to study huge amount of data. In both cases, reading EEG manually is not practical and therefore automatic approach is preferred. In this paper, we propose a simple system that can achieve the state of the art results for IED classification (accuracy of 82%) while using a relatively simple algorithm. The advantage of using a simple algorithm is to make it possible to implement this system on cheap consumer devices like phones.


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