Gender clasification based on single channel EEG signal

2017-11-02
ORAL, EMİN ARGUN
ÖZBEK, İBRAHİM YÜCEL
Çodur, Muhammet Mustafa
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.
2017 International Artificial Intelligence and Data Processing Symposium (IDAP)

Suggestions

Face identification, gender and age groups classifications for semantic annotation videos
Yaprakkaya, Gökhan; Çiçekli, Fehime Nihan; Department of Computer Engineering (2010)
This thesis presents a robust face recognition method and a combination of methods for gender identification and age group classification for semantic annotation of videos. Local binary pattern histogram which has 256 bins and pixel intensity differences are used as extracted facial features for gender classification. DCT Mod2 features and edge detection results around facial landmarks are used as extracted facial features for age group classification. In gender classification module, a Random Trees classif...
Gendered Interactions Mediated by Design: Sexual Harassment on Public Transport
Kaygan, Pınar; Kaygan, Harun; oezguer Keysan, Asuman (2022-11-01)
This paper explores the gendered interactions that are mediated by designed products in actual use contexts. Our case is vehicle design for public transportation, a product category that is, from the outset, relatively gender-neutral when compared to explicitly gender-segregated categories such as household electronics, cars, and toys, even if public transit users are more often women than men. The empirical basis of research comes from interviews with women passengers. Our analysis demonstrates that seemin...
Sleep stage classification based on filter bank optimization
ORAL, EMİN ARGUN; Çodur, Muhammet Mustafa; ÖZBEK, İBRAHİM YÜCEL (2017-12-01)
Sleep stage binary classification is studied using single channel EEG signals. The proposed approach is composed of two steps. In the first step, cepstrum coefficients based features are obtained from EEC signals using a filter bank approach which is tuned for sleep stage classification in terms of number of filters and their type. In the second step, these features are used with support vector machine approach for classification. It is observed that obtained results are comparable with the published result...
Gender classification using mesh networks on multiresolution multitask fMRI data
Ertugrul, Itir Onal; Ozay, Mete; Yarman Vural, Fatoş Tunay (Springer Science and Business Media LLC, 2020-04-01)
Brain connectivity networks have been shown to represent gender differences under a number of cognitive tasks. Recently, it has been conjectured that fMRI signals decomposed into different resolutions embed different types of cognitive information. In this paper, we combine multiresolution analysis and connectivity networks to study gender differences under a variety of cognitive tasks, and propose a machine learning framework to discriminate individuals according to their gender. For this purpose, we estim...
Cepstrum coefficients based sleep stage classification
Oral, Emin Argun; Çodur, Muhammet Mustafa; Özbek, İbrahim Yücel (null; 2017-12-12)
This paper examines filterbank parameters to extract discriminative cepstrum coefficient from EEG signals for sleep stage classification using well-known Support Vector Machine (SVM) algorithm. The proposed method has three main stages as feature extraction, training and classification. In feature extraction step, features are obtained using linear frequency cepstrum coefficients (LFCC) of EEG signals. Then SVM classifier is trained based on the extracted features. In the final step of classification, the c...
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.