TY - GEN
T1 - A comparative analysis of emotion recognition from stimulated EEG signals
AU - Singh, Garima
AU - Jati, Arindam
AU - Khasnobish, Anwesha
AU - Bhattacharyya, Saugat
AU - Konar, Amit
AU - Tibarewala, D. N.
AU - Janarthanan, R.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - This paper proposes a scheme to utilize the unaltered direct outcome of brain’s activity viz. EEG signals for emotion detection that is a prerequisite for the development of an emotionally intelligent system. The aim of this work is to classify the emotional states experimentally elicited in different subjects, by extracting their features for the alpha, beta, and theta frequency bands of the acquired EEG data using PSD, EMD, wavelet transforms, statistical parameters, and Hjorth parameters and then classifying the same using LSVM, LDA, and kNN as classifiers for the purpose of categorizing the elicited emotions into the emotional states of neutral, happy, sad, and disgust. The experimental results being a comparative analysis of the different classifier performances equip us with the best accurate means of emotion recognition from the EEG signals. For all the eight subjects, neutral emotional state is classified with an average classification accuracy of 81.65%, highest among the other three emotions. The negative emotions including sad and disgust have better average classification accuracy of 76.20 and 74.96% as opposed to the positive emotion i.e., happy emotional state, the average classification accuracy of which turns out to be 73.42%.
AB - This paper proposes a scheme to utilize the unaltered direct outcome of brain’s activity viz. EEG signals for emotion detection that is a prerequisite for the development of an emotionally intelligent system. The aim of this work is to classify the emotional states experimentally elicited in different subjects, by extracting their features for the alpha, beta, and theta frequency bands of the acquired EEG data using PSD, EMD, wavelet transforms, statistical parameters, and Hjorth parameters and then classifying the same using LSVM, LDA, and kNN as classifiers for the purpose of categorizing the elicited emotions into the emotional states of neutral, happy, sad, and disgust. The experimental results being a comparative analysis of the different classifier performances equip us with the best accurate means of emotion recognition from the EEG signals. For all the eight subjects, neutral emotional state is classified with an average classification accuracy of 81.65%, highest among the other three emotions. The negative emotions including sad and disgust have better average classification accuracy of 76.20 and 74.96% as opposed to the positive emotion i.e., happy emotional state, the average classification accuracy of which turns out to be 73.42%.
KW - Electroencephalogram (EEG)
KW - Emotion recognition
KW - Empirical mode decomposition (EMD)
KW - Hjorth parameters
KW - K-nearest neighbor (kNN)
KW - Linear discriminant analysis (LDA)
KW - Linear support vector machine (LSVM)
KW - Power spectral density (PSD)
KW - Statistical parameters (STAT)
KW - Wavelet transform (WT)
UR - http://www.scopus.com/inward/record.url?scp=84928027883&partnerID=8YFLogxK
U2 - 10.1007/978-81-322-1602-5_116
DO - 10.1007/978-81-322-1602-5_116
M3 - Conference contribution
AN - SCOPUS:84928027883
T3 - Advances in Intelligent Systems and Computing
SP - 1109
EP - 1115
BT - 2nd International Conference on Soft Computing for Problem Solving, SocProS 2012, Proceedings
A2 - Babu, B.V.
A2 - Nagar, Atulya
A2 - Bansal, Jagdish Chand
A2 - Pant, Millie
A2 - Deep, Kusum
A2 - Ray, Kanad
A2 - Gupta, Umesh
PB - Springer Verlag
T2 - 2nd International Conference on Soft Computing for Problem Solving, SocProS 2012
Y2 - 28 December 2012 through 30 December 2012
ER -