Abstract—IIn this study, Artificial Neural Networks and Particle Swarm Optimization (PSO) techniques designed in the form of a hybrid structure are used for diagnosis of epilepsy patients via EEG signals. Attributes of EEG signals are needed to be determined by employing EEG signals which are recorded using EEG. From this data, four characteristics are extracted for the classification process. 20% of available data is reserved for testing while 80% of available data is being reserved for training. These actions were repeated five times by performing cross-validation process. PSO is used for updating the weights during training ANN and a program is constituted for classification of EEG signals. Education and recording processes were performed with different parameters by means of the constituted program. The obtained findings show that the proposed method was effective for achieving accurate results as much as possible with the use of ANN and PSO, together.
Index Terms—Artificial neural network, particle swarm optimization, epilepsy, EEG signals.
The authors are with the Department of Computer Engineering, Faculty of Engineering and Architecture, Selcuk University, 42079, Konya-Turkey (Corresponding author. Tel.: +00903322234631, e-mail: semaarslan@selcuk.edu.tr).
Cite: Sem a Arslan, Gulay Tezel, and Hakan Islk, "EEG Signals Classification Using a Hybrid Structure of ANN and PSO," International Journal of Future Computer and Communication vol. 1, no. 2, pp. 170-172, 2012.
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