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Multi Channel EEG Based Biometric System with a Custom Designed Convolutional Neural Network

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 436)

Abstract

In this study, a convolutional neural network (CNN) is designed to identify multi-channel raw electroencephalograph (EEG) signals obtained from different subjects. The dataset contains 14 channel EEG signals taken from 21 subjects with their eyes closed at a resting state for 120 s with 12 different stimuli. The resting state EEG waves were selected due to better performance in classification. For the classification, a Convolutional Neural Network (CNN) was custom designed to offer the best performance. With the sliding window approach, the signals were separated into overlapping 5 s windows for training CNN better. fivefold cross-validation was used to increase the generalization ability of the network. It has been observed that, while the proposed CNN is found to give a correct classification rate (CCR) of 72.71%, the CCR reached the level of average 83.51% by using 4 channels. Also, this reduced the training time from 626 to 306 s. Therefore, the results show that usage of specific channels increases the classification accuracy and reduces the time required for training.

Keywords

  • Electroencephalograph
  • Biometric systems
  • Multi-channel
  • Convolutional
  • Neural networks

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  • DOI: 10.1007/978-3-031-01984-5_10
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Acknowledgements

This study is funded by the Scientific and Technological Research Council of Turkey (TUBITAK-2244) Grant no: 119C171.

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Correspondence to Kaan Bakırcıoglu .

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Bakırcıoglu, K., Tanimu, M.B., Özkurt, N., Seçmen, M., Güzeliş, C., Yıldız, O. (2022). Multi Channel EEG Based Biometric System with a Custom Designed Convolutional Neural Network. In: Seyman, M.N. (eds) Electrical and Computer Engineering. ICECENG 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-031-01984-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-01984-5_10

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