Abstract
Emotion plays an important role in the daily life of man and is an important feature of human interaction. Because of its role of adaptation, it motivates people to respond quickly to stimuli in their environment to improve communication, learning and decision making. With the increasing role of the brain-computer interface (BCI) in user-computer interaction, automatic recognition of emotions has become an area of interest in the last decade. The recognition of emotions could be facial expression, gesture, speech and text and could be recorded in different ways, such as electroencephalogram (EEG), positron emission tomography (PET), magnetic resonance imaging (MRI), etc. In this research work, feature extraction feature reduction and classification of emotions have been evaluated on different methods to recognize and classify different emotional states such as fear, sad, frustrated, happy, pleasant and satisfied from inner emotion EEG signals.
Highlights
Human emotion includes the psychological reaction of a human being to the external world or self-stimulation and the physiological reaction to these psychological reactions
Arousal along Y- axis has been divided into two major classes namely Positive Arousal meaning high and Negative Arousal meaning low keeping the valence state as constant. It results into four major classes of emotions such as High Valence High Arousal (HVHA), Low Valence High Arousal (LVHA), High Valence Low Arousal (HVLA) and Low Valence Low Arousal (LVLA) [11]-[15]
Classification method indicates classifier used for discriminating different emotions, number of channels or electrodes of EEG recording device, frequency bands used for analyzing particular emotion, location of electrodes on brain
Summary
Human emotion includes the psychological reaction of a human being to the external world or self-stimulation and the physiological reaction to these psychological reactions. Emotions are captured by language, behavior and facial expressions [2]. These can be falsely expressed by people. Arousal along Y- axis has been divided into two major classes namely Positive Arousal meaning high and Negative Arousal meaning low keeping the valence state as constant. It results into four major classes of emotions such as High Valence High Arousal (HVHA), Low Valence High Arousal (LVHA), High Valence Low Arousal (HVLA) and Low Valence Low Arousal (LVLA) [11]-[15].
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