title: A tensor-based scheme for stroke patients' motor imagery EEG analysis in BCI-FES rehabilitation training.
abstract: BACKGROUND
  Stroke is one of the most common disorders among the elderly. A practical problem in stroke rehabilitation systems is that how to separate motor imagery patterns from electroencephalographic (EEG) recordings. There is a sharp decline in performance of these systems when classical algorithms, such as Common Spatial Pattern (CSP), are directly applied on stroke patients.
  
  
  METHODS
  We propose a tensor-based scheme to detect motor imagery EEG patterns in spatial-spectral-temporal domain directly from multidimensional EEG constructed by wavelet transform method. Discriminative motor imagery EEG patterns are obtained by Fisher score strategy. Furthermore, the most contributed channel groups and frequency bands are selected from these patterns and utilized as prior knowledge for the following motor imagery tasks.
  
  
  RESULTS
  We evaluate our scheme based on EEG datasets recorded from stroke patients. The results show that our method outperforms five other traditional methods in both online and offline recognition performance.
  
  
  METHODS
  Unlike the existing methods, motor imagery EEG patterns in spatial-spectral-temporal domain are simultaneously obtained by our method, preserving the structural information of the multi-channel time-varying EEG.
  
  
  CONCLUSIONS
  Our scheme is encouraged to be transferred to some other practical rehabilitation applications for its better performance.
authors.affiliation: MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: lyyx000@sjtu.edu.cn.
authors.forename: Ye
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai 200040, China.
authors.forename: Mingfen
authors.initials: M
authors.lastname: Li
authors.affiliation: MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.affiliation: MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Hang
authors.initials: H
authors.lastname: Wang
authors.affiliation: MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Junhua
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai 200040, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Jia
authors.affiliation: Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai 200040, China. Electronic address: wuyi4000@163.com.
authors.forename: Yi
authors.initials: Y
authors.lastname: Wu
authors.affiliation: MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: zhang-lq@cs.sjtu.edu.cn.
authors.forename: Liqing
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007092:Imagination; D008297:Male; D009043:Motor Activity; D009044:Motor Cortex / Q000503:physiopathology; D011597:Psychomotor Performance; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000503:physiopathology; D000071939:Stroke Rehabilitation; D060388:Support Vector Machine; D013997:Time Factors; D016896:Treatment Outcome; D063367:Virtual Reality Exposure Therapy
keywords: Brain–computer interface; Electroencephalogram; Rehabilitation; Tensor factorization