An Incremental Version of L-MVU for the Feature Extraction of MI-EEG

• Published in: Computational intelligence and neuroscience Vol. 2019; no. 2019; pp. 1 - 19
• Main Authors: Li, Mingai, Zhu, Xiaoqing, Xi, Hongwei
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Brain-Computer Interfaces; Classification; Datasets; Discriminant analysis; EEG; Electroencephalography; Electroencephalography - methods; Feature extraction; Humans; Imagery; Imagination; Mathematical analysis; Mental task performance; Neurosciences; Noise; Pattern recognition; Representations; Sample variance; Semidefinite programming; Signal Processing, Computer-Assisted; Statistical methods; Synchronism; Synchronization; Training; Wavelet Analysis; Wavelet transforms; China

Due to the nonlinear and high-dimensional characteristics of motor imagery electroencephalography (MI-EEG), it can be challenging to get high online accuracy. As a nonlinear dimension reduction method, landmark maximum variance unfolding (L-MVU) can completely retain the nonlinear features of MI-EEG...