Gender clasification based on single channel EEG signal

Date

2017-11-02

Author

ORAL, EMİN ARGUN
ÖZBEK, İBRAHİM YÜCEL
Çodur, Muhammet Mustafa

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper presents an approach for gender recognition from single channel EEG signal. For this purpose, approximately 24 hour-long EEG data, obtained during daily routine activities including sleep, was used. First, cepstrum coefficients of EEG signals were obtained in the frequency domain to construct the features SET. Second, a machine learning step was performed using these features with Support Vector Machines (SVM). Finally, gender identification was performed on the test data for which features were obtained in the same manner. Based on the initially obtained experimental results, epoc based gender classification success rate of the proposed method is 77.84% for the awake phase of the day while success rate is 89.66% for the sleep phase. Based on these results, it was determined that the biometric discriminative capability of the EEG signal varies at different times of the day.

Subject Keywords

Sleep stages, SVM, Gender classification, EEG signal

URI

https://hdl.handle.net/11511/30044

DOI

https://doi.org/10.1109/idap.2017.8090273

Conference Name

2017 International Artificial Intelligence and Data Processing Symposium (IDAP)

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Citation Formats

E. A. ORAL, İ. Y. ÖZBEK, and M. M. Çodur, “Gender clasification based on single channel EEG signal,” presented at the 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, TURKEY, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30044.