Open AccessApplication of Deep Learning for Emotion Recognition
Author(s) -
Varsha R Toshniwal,
AUTHOR_ID,
Pooja Suresh Puri,
AUTHOR_ID
Publication year - 2021
Publication title -
ymer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.103
H-Index - 5
ISSN - 0044-0477
DOI - 10.37896/ymer20.12/75
Subject(s) - electroencephalography , brain activity and meditation , perception , set (abstract data type) , computer science , brain waves , property (philosophy) , artificial intelligence , pattern recognition (psychology) , human brain , beta (programming language) , speech recognition , psychology , neuroscience , philosophy , epistemology , programming language
The electroencephalogram (EEG) gained a lot of importance in recent years because of its property to depict the nature and actions of human perception. EEG signals are good at capturing the emotional state of a person by measuring the neuronal activities in different regions of the brain. Lots of EEG-based brain-computer interfaces with a different number of channels ( 62 channels, 32 channels, etc.) are being used to capture neuronal activities which can be segmented into different frequency ranges (delta, theta, alpha. beta and gamma). This paper puts forward a neural network architecture for the recognition of emotion from EEG signals and a study providing the set of brain regions and the frequency type associated with the corresponding brain region which contributes most for the detection of emotion though EEG signals. For experimentation, SEED-IV dataset has been used