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

Authors: Wanzeng Kong; Xianghao Kong; Qiaonan Fan; Qibin Zhao; Andrzej Cichocki

Addresses: College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China; Fujian Key Laboratory of Rehabilitation Technology, Fuzhou 350003, China ' College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China ' College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China ' School of Automation, Guangdong University of Technology, Guangdong 511400, China; Tensor Learning Unit, RIKEN AIP, Tokyo 103-0027, Japan ' College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China; Skolkovo Institute of Science and Technology, Moscow 143026, Russia

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.

Keywords: background EEG; task-free; brainprint; sparse representation; maximum correntropy criterion; rational quadratic kernel.

DOI: 10.1504/IJDMB.2019.100629

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.3, pp.280 - 300

Received: 09 May 2019
Accepted: 13 May 2019
Published online: 05 Jul 2019 *

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