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Title: Hybrid support vector machine for classification of EEG signals
Authors: Mohammad Zaini, Ali Omar
Journal: ARPN Journal of Engineering and Applied Sciences
Publisher: Khyber Medical College, Peshawar
Country: Pakistan
Year: 2016
Volume: 11
Issue: 19
Language: English
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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