EEG Signal Feature Extraction and Classification Analysis
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In modern techniques Electroencephalogram (EEG) base emotion recognition is a new area with challenging issue concerning the induction of emotion state diagnosis. There are different techniques for analysis mental diagnosis which helps to further clinical assessment. EEG signal generally contains enormous measures of data with uncountable classifications. The signals turn ought more difficult amid assessing task if the data is captured over a long period. The powerful methodologies are important to secure the hidden and significant data delivered by the action in human cerebrum that covered in...
In modern techniques Electroencephalogram (EEG) base emotion recognition is a new area with challenging issue concerning the induction of emotion state diagnosis. There are different techniques for analysis mental diagnosis which helps to further clinical assessment. EEG signal generally contains enormous measures of data with uncountable classifications. The signals turn ought more difficult amid assessing task if the data is captured over a long period. The powerful methodologies are important to secure the hidden and significant data delivered by the action in human cerebrum that covered inside the signals. In this manner, relevant strategies are produced to developed classify data. There are bunches of past researchers and works related to the previously mentioned task, both feature extraction and classification have not been all around created in accomplishing more prominent precision. In this research, methodologies are proposed for the classification arrangement of EEG which can give high precision. The main object is two pattern recognition methods implemented such as Radial Basis Function, Support Vector Machine to explore the appropriate feature and classification
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