Task-free brainprint recognition based on low-rank and sparse decomposition model

Abstract

Electroencephalography (EEG)-based brainprint recognition was usually completed under a singular task, such as recognition based on visual-evoked potentials. This paper proposes a fast task-free brainprint recognition to break the restriction. We presume a task-related EEG can be divided into the background EEG (BEEG) and the residue EEG. Wherein, BEEG contains one's unique intrinsic brainprint, which was supposed to be a low-rank characteristic. To analyse more precisely, short time Fourier Transform (STFT) are exerted to expand time series EEG into time-frequency domain. Then, a Low-Rank Matrix Decomposition (LRMD)-based algorithm combined with maximum correntropy criterion (MCC) and rational quadratic kernel was designed to extract BEEG. Finally, through sparse representation, BEEG can be classified efficiently. The excellent performance under low rank and various time length scales indicates that our method does not rely on task types and provides a new direction for the application of brainprint recognition.