Hidden-layer visible deep stacking network optimized by PSO for motor imagery EEG recognition
作者: Xianlun TangNa ZhangJialin ZhouQing Liu
作者单位: 1Key Laboratory of Network Control & Intelligent Instrument (Chongqing University of Posts and Telecommunications), Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
刊名: Neurocomputing, 2016
来源数据库: Elsevier Journal
DOI: 10.1016/j.neucom.2016.12.039
关键词: Deep stacking networkRestricted Boltzmann machineParticle swarm optimizationFeature extractionEEG recognition
原始语种摘要: Abstract(#br)A novel method called PSO optimized hidden-layer visible deep stacking network (PHVDSN) is proposed for feature extraction and recognition of motor imagery electroencephalogram (EEG) signals. A prior knowledge is introduced into the intermediate layer of deep stacking network (DSN) and the hidden nodes are expanded by the unsupervised training of restricted Boltzmann machine (RBM) for the parameter initialization. Then particle swarm optimization (PSO) is applied to optimize the input weights, aiming at alleviating the risk of being immersed in the curse of dimensionality. The performance of the proposed method is evaluated with real EEG signals from different subjects. Experimental results show that the recognition accuracy of PHVDSN is superior to some state-of-the-art...
全文获取路径: Elsevier  (合作)
分享到:
来源刊物:
影响因子:1.634 (2012)

×