EEG AUTHENTICATION BASED ON DEEP LEARNING OF TRIPLET LOSS

Abstract

As a novel biometric characteristic, the electroencephalogram (EEG) is used for biometric authentication. To solve the challenge of efficiently growing the number of classifications in traditional classification networks and to increase the practicality of engineering, this paper proposes an authentication approach for EEG data based on an attention mechanism and a triplet loss function. The method begins by feeding EEG signals into a deep convolutional network, maps them to 512-dimensional Euclidean space using a long short-term memory network combined with an attention mechanism, and obtains feature vectors for EEG signals with identity information; it then adjusts the network parameters using a triplet loss function, such that the Euclidean distance between feature vectors of similar signals decreases while the distance between signals of a different type increases. Finally, the recognition method is evaluated using publicly available EEG data sets. The experimental results suggest that the method maintains the recognition rate while effectively expanding the classifications of the model, hence thus boosting the practicability of EEG authentication.