A Real-Time Approach to Classify EEG Signals for Identifying Prevarication

被引:0
作者
Nandhini Kesavan
Narasimhan Renga Raajan
机构
[1] SASTRA University,
来源
National Academy Science Letters | 2019年 / 42卷
关键词
EEG; MLP; Bagging; P300; Classification;
D O I
暂无
中图分类号
学科分类号
摘要
Electroencephalography (EEG) is a recording method which captures brain action. In these frameworks, clients unequivocally control their brain action as opposed to utilizing motor movements to create signals that can be utilized to control computers or specialized gadgets. In this research, classifiers such as multilayer perceptron and bagging are utilized to quantify the exactness and accuracy of the acquired mind information. The percentage of recognition plays a major role as it indicates the person, the ratio he is in synch with viewing and thinking. EEG signal and P300 are used to measure the recordings done in the brain. On comparing the results of EEG and P300, it was found that recognition rate was good with the latter.
引用
收藏
页码:33 / 37
页数:4
相关论文
共 71 条
[1]  
Subasi A(2010)EEG signal classification using PCA, ICA, LDA and support vector machines Expert Syst Appl 37 8659-8666
[2]  
Ismail Gursoy M(2014)Simultaneous recording of MEG, EEG and intracerebral EEG during visual stimulation: from feasibility to single-trial analysis NeuroImage 99 548-558
[3]  
Dubarry AS(2014)Automatic reference selection for quantitative EEG interpretation: Identification of diffuse/localised activity and the active earlobe reference, iterative detection of the distribution of EEG rhythms Med Eng Phys 36 88-95
[4]  
Badier JM(2015)Interaction between words and symbolic gestures as revealed by N400 Brain Topogr 28 591-605
[5]  
Trébuchon-Da Fonseca A(2013)Electroencephalograms for ubiquitous Robotic Systems Proc Comput Sci 21 174-182
[6]  
Gavaret M(2009)Combined neural network model employing wavelet coefficients for EEG signals classification Dig Sig Process 19 297-308
[7]  
Carron R(2014)Epilieptic seizure detection by analyzing EEG signals using different transformation techniques Neurocomputing. 145 190-200
[8]  
Bartolomei F(2012)Retrospection of SVM Classifier J Theor Appl Inf Technol 38 83-88
[9]  
Liégeois-Chauvel C(2014)An experimental evaluation of novelty detection methods Neurocomputing 135 313-327
[10]  
Régis J(2015)Efficient classification system based on fuzzy-rough feature selection and multitree genetic programing for intension pattern recognition using brain signal Expert Syst Appl 42 1644-1651