Open AccessClassification of Emotions through EEG Signals using SVM and DNN
Author(s) -
Veena M. N,
S. Mahalakshmi
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i8103.029420
Subject(s) - electroencephalography , support vector machine , brain–computer interface , computer science , interface (matter) , entertainment , artificial intelligence , emotion recognition , artificial neural network , speech recognition , pattern recognition (psychology) , psychology , neuroscience , art , bubble , maximum bubble pressure method , parallel computing , visual arts
Emotions are important for Humans both at work place and in their life. Emotions helps us to communicate with others, to take decisions, in understand others etc., Emotions recognition not only helps us to solve the mental illness but also are important in various application such as Brain Computer Interface , medical care and entertainment This paper mainly deals with how Emotions are Classified through EEG Signals using SVM (Support Vector machine) and DNN (Deep Neural Networks) . Applying the most appropriate algorithm to detect the emotional state of a person and play the corresponding song in the playlist. Brain signals can be collected using EEG (electroencephalography) devices