Detection of P300 based on Artficial Bee Colony

Süleyman Abdullah Aytekin, Tuba Kiyan


A Brain-Computer Interface (BCI) is a system that allows users to communicate with their environment through cerebral activity. P300 signal, which is used widely in BCI applications, is produced as a response to a stimulus and can be measured in the parietal lobe of the brain. In this paper, an approach which is a swarm intelligence technique, called Artificial Bee Colony (ABC) together with Multilayer Perceptron (MLP) is used for the detection of P300 signals to achieve high accuracy. The system is based on the P300 evoked potential and is tested on four healthy subjects. It has two main blocks, feature extraction and classification. In the feature extraction block, Power Spectrum Density (PSD) is used whereas ABC was employed to train Multi Layer Perceptron (MLP) in the classification part. This method is compared to other methods such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). The best result that is achieved in this work is 99.8%.


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Paper Citation

in Harvard Style

Aytekin S. and Kiyan T. (2016). Detection of P300 based on Artficial Bee Colony . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 183-189. DOI: 10.5220/0005696001830189

in Bibtex Style

author={Süleyman Abdullah Aytekin and Tuba Kiyan},
title={Detection of P300 based on Artficial Bee Colony},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},

in EndNote Style

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Detection of P300 based on Artficial Bee Colony
SN - 978-989-758-170-0
AU - Aytekin S.
AU - Kiyan T.
PY - 2016
SP - 183
EP - 189
DO - 10.5220/0005696001830189