EEG-Based Emotion Recognition Using Statistical Measures and Auto-Regressive Modeling

Aravind E. Vijayan · Deepak Sen · A. P. Sudheer

88 citations0/0 files storedInternational Conference on Computational Intelligence & Communication Technology (CICT)

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Face and Expression Recognition

Abstract

In this paper, a novel approach towards classification of various human emotions based on statistically weighed autoregressive modeling of Electroencephalogram (EEG) signals is discussed. The proposed algorithm was proven to be superior to many related works, in distinguishing different emotions such as happiness, fear, sadness etc. The findings discussed are based on the results obtained using benchmark emotion based EEG database called DEAP. In this work, epochs were extracted from data using statistical measures such as Shannon Entropy and higher order auto-regressive model was fit to the extracted features. The model was used for classifying human emotions by feeding it into a multi-class Support Vector Machine (MCSVM). The proposed algorithm is proven to be more efficient than existing algorithms as a classification accuracy of 94.097% was obtained.

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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2009Emotion Recognition From EEG Using Higher Order Crossings 2010Classification of human emotion from EEG using discrete wavelet transform 2009Emotion classification based on gamma-band EEG 2013A Review on the Computational Methods for Emotional State Estimation from the Human EEG 2007EEG evidence for mirror neuron activity during the observation of human and robot actions: Toward an analysis of the human qualities of interactive robots 2009EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine 2009Emotion recognition using EEG signals with relative power values and Bayesian network

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2018EEG-based classification of emotions using empirical mode decomposition and autoregressive model 2022CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis 2020Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy 2015Identifying valence and arousal levels via connectivity between EEG channels

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