Age and gender classification using brain-computer interface

With the development of Internet of things (IOT), it is now possible to connect various heterogeneous devices together using Internet. The devices are able to share their information for various applications including health care, security and monitoring. IOT facilitates patients to self-monitor their physiological states invariably and doctors to monitor their patients remotely. Electroencephalography (EEG) provides a monitoring method to record such electrical activities of the brain using sensors. In this paper, we present an automatic age and gender prediction framework of users based on their neurosignals captured during eyes closed resting state using EEG sensor. Using EEG sensor, brain activities of 60 individuals with different age groups varying between 6 and 55 years and gender (i.e., male and female) have been recorded using a wireless EEG sensor. Discrete wavelet transform frequency decomposition has been performed for feature extraction. Next, random forest classifier has been applied for modeling the brain signals. Lastly, the accuracies have been compared with support vector machine and artificial neural network classifiers. The performance of the system has been tested using user-independent approach with an accuracy of 88.33 and 96.66% in age and gender prediction, respectively. It has been analyzed that oscillations in beta and theta band waves show maximum age prediction, whereas delta rhythm leads to highest gender classification rates. The proposed method can be extended to different IOT applications in healthcare sector where age and gender information can be automatically transmitted to hospitals and clinics through Internet.