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An EEG Based Emotion Recognition and Classification Using Machine Learning Techniques

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Document pages: 7 pages

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.

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