TY - JOUR
AU - Li, Hongliang
AB - ISSN 0146-4116, Automatic Control and Computer Sciences, 2019, Vol. 53, No. 5, pp. 452–460. © Allerton Press, Inc., 2019. ELM_Kernel and Wavelet Packet Decomposition Based EEG Classif ication Algorithm a, b, a a a a Li Wang *, Zhi Lan , Qiang Wang , Rong Yang , and Hongliang Li National Research Center for Rehabilitation Technical Aids, BDA, China Qinhuangdao Institute of National Research Center for Rehabilitation Technical Aids, Funing Economic and Technological Development Zone, Qinhuangdao, China *e-mail: wangli@nrcrta.cn Received October 29, 2018; revised March 25, 2019; accepted March 29, 2019 Abstract—Rehabilitation technology based on brain-computer interface (BCI) has become a promis- ing approach for patients with dyskinesia to regain movement. In this paper, a novel classification algorithm is proposed based on the characteristic of electroencephalogram (EEG) signals. Specif ically wavelet packet decomposition (WPD) and Extreme learning machine with kernel (ELM_Kernel) algorithm are studied. In view of the existence of cross-banding of WPD, the average energy of the wavelet packets of the corresponding frequency bands which belong to the mu and beta rhythm are used to form the feature vectors that are classified by the ELM_Kernel algorithm. Simulation results demonstrate that the proposed algorithm produces a high probability of 
TI - ELM_Kernel and Wavelet Packet Decomposition Based EEG Classification Algorithm
JF - Automatic Control and Computer Sciences
DO - 10.3103/S0146411619050079
DA - 2019-11-18
UR - https://www.deepdyve.com/lp/springer-journals/elm-kernel-and-wavelet-packet-decomposition-based-eeg-classification-H9Tecmihvx
SP - 452
EP - 460
VL - 53
IS - 5
DP - DeepDyve
ER -