Authors :
Divya Y S ,Deepika T R ,Jnanadevi R T Shredevi Nagnur ,Tejaswini C
Volume/Issue :
Volume 2 - 2017, Issue 6 - June
Google Scholar :
https://goo.gl/wXv0R0
Scribd :
https://goo.gl/MpnKhh
Thomson Reuters ResearcherID :
https://goo.gl/3bkzwv
Abstract :
This paper presents the classification of EEG signal using the deep machine learning and implementing the application on the FPGA.EEG signal analysis is such an important thing for disease analysis and brain– computer analysis. Electroencephalography (EEG) monitoring the state of the user’s brain functioning and treatment for any psychological disorder. Using this way we will be able to find the accurate outputs of the expected results. This is achieved by training the artificial neural network in MATLAB application. This algorithm uses wavelet transform and neural network for training the artificial neurons.Deep machine learning algorithm will require massive data for feeding into our models.
Keywords :
Brain comuting interface,Deep learning,wavelet transform,field programmable gated array, Electroencephalography,artifical neurons.
This paper presents the classification of EEG signal using the deep machine learning and implementing the application on the FPGA.EEG signal analysis is such an important thing for disease analysis and brain– computer analysis. Electroencephalography (EEG) monitoring the state of the user’s brain functioning and treatment for any psychological disorder. Using this way we will be able to find the accurate outputs of the expected results. This is achieved by training the artificial neural network in MATLAB application. This algorithm uses wavelet transform and neural network for training the artificial neurons.Deep machine learning algorithm will require massive data for feeding into our models.
Keywords :
Brain comuting interface,Deep learning,wavelet transform,field programmable gated array, Electroencephalography,artifical neurons.
