Abstract
Brainprint is a new type of biometric in the form of EEG, directly linking to intrinsic identity. Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task. Due to the ability to extract high-level features and latent dependencies, deep learning can effectively overcome the limitation of specific tasks, but numerous samples are required for model training. Therefore, brainprint recognition in realistic scenes with multiple individuals and small amounts of samples in each class is challenging for deep learning. This article proposes a Convolutional Tensor-Train Neural Network (CTNN) for the multi-task brainprint recognition with small number of training samples. Firstly, local temporal and spatial features of the brainprint are extracted by the convolutional neural network (CNN) with depthwise separable convolution mechanism. Afterwards, we implement the TensorNet (TN) via low-rank representation to capture the multilinear intercorrelations, which integrates the local information into a global one with very limited parameters. The experimental results indicate that CTNN has high recognition accuracy over 99% on all four datasets, and it exploits brainprint under multi-task efficiently and scales well on training samples. Additionally, our method can also provide an interpretable biomarker, which shows specific seven channels are dominated for the recognition tasks.
Highlights
T HE demand for identity authentication in the fields of information security and financial security is growing
convolutional neural network (CNN) [33] and CNN-RNN [34] are introduced to our work as the baseline model, which are widely deployed in the EEG-based biometric identification task
The CNN structure is composed of two convolution layers and two fully connected layers, and CNN-RNN is a cascade convolutional recurrent neural networks, that the CNN is cascaded with the Long Short-Term Memory (LSTM) or GRU
Summary
T HE demand for identity authentication in the fields of information security and financial security is growing. Traditional recognition technologies include: fingerprint recognition, palmprint recognition, facial recognition, iris recognition, gait recognition, etc. In 2015, Armstrong et al [2] proposed “brainprint” as a new recognition physiological feature, which is based on a composite map of specific brain electrical signals. In 2016, researchers at Binghamton University in New York [3] successfully used it as a identifying biometric, confirming the effectiveness of brain fingerprint recognition. “Brainprints” exist in the brain in the form of electroencephalogram (EEG) and directly link to individual identity. When the individual dies, his/her EEG characteristics will disappear. Because of these good characteristics, brain fingerprint recognition can be widely used in places that require high security
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