Emotion recognition from EEG brain signals based on particle swarm optimization and genetic search

The purpose of this study is to classify the emotions from human brain signals using electroencephalography (EEG). EEG signals were acquired while subject were watching the emotional stimuli. Subjects were asked to watch four types of emotional stimulus such as, happy, calm, sad and scared. The EEG signals were recorded using 14-channel brain headset. We preprocessed the EEG recorded data with manual artifact rejection and independent component analysis (ICA). The total numbers of 21 subjects were participated in this experiment. We performed emotion recognition which was based on two feature selection methods such as, particle swarm optimization (PSO) and genetic search (GS). Further, the selected features were processed using support vector machine (SVM). Emotion recognition accuracy had shown the possibility of classification of EEG brain activity.

[1]  Konstantinos N. Plataniotis,et al.  Affect recognition using EEG signal , 2012, 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP).

[2]  P. Lang,et al.  International Affective Picture System (IAPS): Instruction Manual and Affective Ratings (Tech. Rep. No. A-4) , 1999 .

[3]  Riccardo Poli,et al.  Geometric Particle Swarm Optimisation , 2007, EuroGP.

[4]  B. Hjorth.  The physical significance of time domain descriptors in EEG analysis. , 1973, Electroencephalography and clinical neurophysiology.

[5]  Hyo Jong Lee,et al.  EEG based Emotion Recognition from Human Brain using Hjorth Parameters and SVM , 2015, BSBT 2015.

[6]  Koji Inui,et al.  Inner experience of pain: imagination of pain while viewing images showing painful events forms subjective pain representation in human brain. , 2006, Cerebral cortex.

[7]  L. Aftanas,et al.  Event-Related Synchronization and Desynchronization During Affective Processing: Emergence of Valence-Related Time-Dependent Hemispheric Asymmetries in Theta and Upper Alpha Band , 2001, The International journal of neuroscience.

[8]  M. Shamim Hossain,et al.  Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring , 2016, Comput. Networks.

[9]  M. Shamim Hossain,et al.  Cloud-Supported Cyber–Physical Localization Framework for Patients Monitoring , 2017, IEEE Systems Journal.

[10]  B. Hjorth.  EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[11]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[12]  M. Shamim Hossain,et al.  Cloud-Assisted Speech and Face Recognition Framework for Health Monitoring , 2015, Mobile Networks and Applications.

[13]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[14]  L. Aftanas,et al.  Affective picture processing: event-related synchronization within individually defined human theta band is modulated by valence dimension. , 2001, Neuroscience Letters.

[15]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  Aamir Saeed Malik,et al.  Analysis of EEG signals regularity in adults during video game play in 2D and 3D , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[19]  Raja Majid Mehmood,et al.  Alpha Activity during Emotional Experience Revealed by ERSP , 2014 .

[20]  Hyo Jong Lee,et al.  Exploration of Prominent Frequency Wave in EEG Signals from Brain Sensors Network , 2015, Int. J. Distributed Sens. Networks.

[21]  M. Shamim Hossain,et al.  Cloud-Based Collaborative Media Service Framework for HealthCare , 2014, Int. J. Distributed Sens. Networks.

[22]  L. Aftanas,et al.  Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency bands. , 2002, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[23]  Johan Hagelbäck,et al.  Evaluating Classifiers for Emotion Recognition Using EEG , 2013, HCI.

[24]  M. Shamim Hossain,et al.  Audio-Visual Emotion Recognition Using Big Data Towards 5G , 2016, Mob. Networks Appl..

[25]  Muhammad Al-Qurishi,et al.  Evaluating the impact of a cloud-based serious game on obese people , 2014, Comput. Hum. Behav..