{"title":"五种神经网络方法对两种精神状态脑电信号分类的性能比较","authors":"V. Khare, J. Santhosh, S. Anand","doi":"10.1109/INDCON.2008.4768792","DOIUrl":null,"url":null,"abstract":"The Paper demonstrate the comparison of performance by five artificial neural network (ANN) technique (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum for classification of planning of right hand movement with respect to an awake relaxed state. Wavelet packet transform (WPT) was used for Feature extraction of the relevant electroencephalogram (EEG) signals.","PeriodicalId":196254,"journal":{"name":"2008 Annual IEEE India Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Performance comparison using five ANN methods for classification of EEG signals of two mental states\",\"authors\":\"V. Khare, J. Santhosh, S. Anand\",\"doi\":\"10.1109/INDCON.2008.4768792\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Paper demonstrate the comparison of performance by five artificial neural network (ANN) technique (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum for classification of planning of right hand movement with respect to an awake relaxed state. Wavelet packet transform (WPT) was used for Feature extraction of the relevant electroencephalogram (EEG) signals.\",\"PeriodicalId\":196254,\"journal\":{\"name\":\"2008 Annual IEEE India Conference\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Annual IEEE India Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDCON.2008.4768792\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Annual IEEE India Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDCON.2008.4768792","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance comparison using five ANN methods for classification of EEG signals of two mental states
The Paper demonstrate the comparison of performance by five artificial neural network (ANN) technique (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum for classification of planning of right hand movement with respect to an awake relaxed state. Wavelet packet transform (WPT) was used for Feature extraction of the relevant electroencephalogram (EEG) signals.