An EEG Based Emotion Recognition and Classification Using Machine Learning Techniques

International Journal of Emerging Technology and Innovative Engineering, Volume 5, Issue 4, April 2019

11 Pages Posted: 22 Apr 2019

See all articles by Soundarya S

Soundarya S

Sri Krishna College of Technology

Date Written: April 12, 2019

Abstract

Emotions are complex phenomena that play significant roles in the quality of human life. Emotion plays a major role in motivation, perception, cognition, creativity, attention, learning and decision-making. A major problem in understanding emotion is the assessment of the definition of emotions. According to the WHO, every year, almost one million people die from suicide. Suicide is a leading cause of death among teenagers and adults. Existing techniques uses simple keyword search method to find emotional content in blog data and identify bloggers at risk of suicide. However, Deep sentiment analysis in suicide notes has not yet been explored much with computational approaches using advanced Machine Learning and Natural Language Processing techniques. The main contribution of the proposed work employs Electroencephalography (EEG) based psychological states for initializing the parameter weights of the neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. The Synchronized brainwave dataset contains electroencephalogram (EEG) signal values and details of the patient. The proposed methodology using Machine learning techniques to detect emotion will help individuals, industry, educational institution and Government organization to take decisions and helps people to be more comfortable in expressing their problems.

Keywords: Electroencephalography, Sentiment, Psychological, Deep learning

Suggested Citation

S, Soundarya, An EEG Based Emotion Recognition and Classification Using Machine Learning Techniques (April 12, 2019). International Journal of Emerging Technology and Innovative Engineering, Volume 5, Issue 4, April 2019 . Available at SSRN: https://ssrn.com/abstract=3370869

Soundarya S (Contact Author)

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India

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