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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5607
Title: EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model
Authors: Kaushik P.
Gupta A.
Pratim Roy, Partha
Dogra D.P.
Published in: IEEE Sensors Journal
Abstract: With the rapid development of brain-computer interfaces (BCI), the number of applications that use BCI technology is increasingly thick and fast. Prediction of age and gender of a person through EEG analysis is a new application of BCI that has been proposed in this paper. An industry standard EEG recording device has been used to record cerebral activities of 60 subjects (both male and female) in relaxed position with closed eyes. Deep BLSTM-LSTM network has been used to construct a hybrid learning framework for the aforementioned analysis. Accuracy of 93.7% and 97.5% have been recorded for age and gender classification problems respectively. These values are better than the state-of-the-art methods. Our analysis also reveals that the beta band frequencies are better in predicting the age and gender as compared to other frequency bands of the EEG signals. The proposed method has several applications, including biometric, health-care, entertainment, and targeted advertisements. © 2001-2012 IEEE.
Citation: IEEE Sensors Journal (2019), 19(7): 2634-2641
URI: https://doi.org/10.1109/JSEN.2018.2885582
http://repository.iitr.ac.in/handle/123456789/5607
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Age detection
BCI
BLSTM
deep learning
EEG
gender detection
ISSN: 1530437X
Author Scopus IDs: 57195478446
57214417501
56880478500
35408975400
Author Affiliations: Kaushik, P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, 247667, India
Gupta, A., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, 247667, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, 247667, India
Dogra, D.P., School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, 752050, India
Corresponding Author: Kaushik, P.; Department of Computer Science and Engineering, Indian Institute of TechnologyIndia; email: pkaushik@cs.iitr.ac.in
Appears in Collections:Journal Publications [CS]

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