Subjects identification using EEG-recorded imagined speech

Due to the problems presented in current traditional/biometric security systems, the interest to use new security systems, have been increasing. This paper explores the use of brain signals EEG-based during imagined speech in order to use it as a new biometric measure for Subjects identification and thus create a new biometric security system. The main contribution of this paper are two methods for feature extraction, first to improve the signal-to-noise ratio the Common Average Reference was applied. The first method was based on Discrete Wavelet Transform, and the second method was based on statistical features directly from the raw signal. The proposed methods were tested in a dataset of 27 Subjects who performed 33 repetitions of 5 imagined words in Spanish. The results show the feasibility of the task with accurate identification of the Subject, regardless of the imagined word used and using a commercial EEG system (EMOTIV EPOC). In addition, the scope of the method is displayed by decreasing the training data, as well as the number of active sensors for the identification task. Using the proposed method with future improvements and implementing it in a low-cost device can be a new and valuable biometric security system.