Abstract
Emotions play an important role in human cognition, perception, decision-making, and interaction. In this paper, Neural Network (NN) based system for human emotions classification by extracting features from Electroencephalogram (EEG) signal is proposed. EEG data for the classification of emotions is obtained from the DEAP database. Extracted more than 30 features from EEG and they are used for the emotion classification. Totally, 33 varieties of features are extracted from EEG data. However, there are reports on voice-based, facial-image-based study of expressions to recognize their emotions. However, emotion identification using both methods can be biased as they can be faked. In order to overcome this difficulty, many researchers analyze brain physiological signals to represent the changing patterns during emotional fluctuations. Neural networks have widely been used in emotion classification. Reported here is the classification with the backpropagation artificial neural network. Experimental results have shown an average accuracy above 94.45% is achieved for all the subjects and regions combined.
| Language | English |
|---|---|
| Title of host publication | Lecture Notes in Electrical Engineering |
| Publisher | Springer Verlag |
| Pages | 297-310 |
| Number of pages | 14 |
| DOIs | |
| Publication status | Published - 01-01-2019 |
Publication series
| Name | Lecture Notes in Electrical Engineering |
|---|---|
| Volume | 478 |
| ISSN (Print) | 1876-1100 |
| ISSN (Electronic) | 1876-1119 |
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All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
Cite this
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Neural network approach for classification of human emotions from EEG signal. / Shashi Kumar, G. S.; Sampathila, Niranjana; Shetty, Harikishan.
Lecture Notes in Electrical Engineering. Springer Verlag, 2019. p. 297-310 (Lecture Notes in Electrical Engineering; Vol. 478).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Neural network approach for classification of human emotions from EEG signal
AU - Shashi Kumar, G. S.
AU - Sampathila, Niranjana
AU - Shetty, Harikishan
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Emotions play an important role in human cognition, perception, decision-making, and interaction. In this paper, Neural Network (NN) based system for human emotions classification by extracting features from Electroencephalogram (EEG) signal is proposed. EEG data for the classification of emotions is obtained from the DEAP database. Extracted more than 30 features from EEG and they are used for the emotion classification. Totally, 33 varieties of features are extracted from EEG data. However, there are reports on voice-based, facial-image-based study of expressions to recognize their emotions. However, emotion identification using both methods can be biased as they can be faked. In order to overcome this difficulty, many researchers analyze brain physiological signals to represent the changing patterns during emotional fluctuations. Neural networks have widely been used in emotion classification. Reported here is the classification with the backpropagation artificial neural network. Experimental results have shown an average accuracy above 94.45% is achieved for all the subjects and regions combined.
AB - Emotions play an important role in human cognition, perception, decision-making, and interaction. In this paper, Neural Network (NN) based system for human emotions classification by extracting features from Electroencephalogram (EEG) signal is proposed. EEG data for the classification of emotions is obtained from the DEAP database. Extracted more than 30 features from EEG and they are used for the emotion classification. Totally, 33 varieties of features are extracted from EEG data. However, there are reports on voice-based, facial-image-based study of expressions to recognize their emotions. However, emotion identification using both methods can be biased as they can be faked. In order to overcome this difficulty, many researchers analyze brain physiological signals to represent the changing patterns during emotional fluctuations. Neural networks have widely been used in emotion classification. Reported here is the classification with the backpropagation artificial neural network. Experimental results have shown an average accuracy above 94.45% is achieved for all the subjects and regions combined.
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U2 - 10.1007/978-981-13-1642-5_27
DO - 10.1007/978-981-13-1642-5_27
M3 - Chapter
T3 - Lecture Notes in Electrical Engineering
SP - 297
EP - 310
BT - Lecture Notes in Electrical Engineering
PB - Springer Verlag
ER -