EEG classification based on a match filter and metric learning
LIU Hongma, WANG Shengjin
National Laboratory for Information Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Abstract：Brain signal analyses and brain-computer interfaces are key topics in human-computer interaction research. Current electroencephalography (EEG) signal classification methods are complicated and difficult to apply in practice. This paper presents a match filter based classification framework using a hypothesis testing model and a match filter which is a linear function of the signal due to the Gaussian noise assumption. A metric learning based method is then used to estimate the principle component and the optimal covariance matrix to further enhance the model discrimination. The results show that this method provides better recognition accuracy with less computational complexity than other algorithms which makes it more practical.
刘宏马, 王生进. 基于匹配滤波器和度量学习的脑电信号分类[J]. 清华大学学报（自然科学版）, 2021, 61(3): 248-253.
LIU Hongma, WANG Shengjin. EEG classification based on a match filter and metric learning. Journal of Tsinghua University(Science and Technology), 2021, 61(3): 248-253.
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