EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model

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.