Methods of Detecting Emotion

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Methods to Detect Emotion

Research successfully identified five different methods of detecting emotions that are backed by research evidence. The methods are Electroencephalogram emotion detection, brain activity emotion detection, three-stage emotion detection based on physiological signals, heart rate method, and movement-based emotion detection. A detailed report is presented below.

ELECTROCEPHALOGRAM (EEG) EMOTION DETECTION METHOD

  • It was expected that the PSD of Alpha waves of amusement and happiness are bigger than what is recorded by the emotions of fear.

EMOTION DETECTION BASED ON BRAIN ACTIVITY

  • In the study, this method is used to measure the emotions like envy, anger, disgust, happiness, gratitude, lust, fear, shame, and pride.
  • The study reported neural activation as the main reason for the recorded differences in brain images. Different emotional words and images handed to the participants resulted to different intensities of brain activity.
  • The emotion detection accuracy level was reported to be 61%-81% for all emotions.

THREE-STAGE EMOTION DETECTION BASED ON PHYSIOLOGICAL SIGNS

  • This method consists of three interlinked stages to eliminate errors and differences.

HEART RATE (HR) METHOD

  • The study on cardiovascular features during emotional responses.
  • This study focused on emotions of fear, anger, and happiness.

RECOGNIZING EMOTION FROM BODY MOVEMENTS

  • This method uses the Feed Forward Deep Convolution Network to predict the human emotion based on body movements.
  • In this method the emphasis is on emotions like happiness, sadness, anger, fear, and untrustworthy.
  • The system uses captured images to determine the emotional state of real-time images of people.

RESEARCH STRATEGY

In order to determine the different methods of detecting emotion that are supported by research, we consulted scholarly publications for scientific studies on emotion detection. We restricted our research to studies published in the last 24 months and found resourceful studies although some of them draw insight from older studies in the same field. Ultimately, our attempt was successful and we found five methods of detecting emotions that are supported by scientific and academic research.
Sources
Sources

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  • "Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods,"
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  • " An accurate understanding of human emotions is necessary for these computers to trigger proper feedback. Among multiple emotional channels, physiological signals are synchronous with emotional responses; therefore, analyzing physiological changes is a recognized way to estimate human emotions. "
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  • "Controlling for this distribution improved valence classification accuracy from 56% to 85% and arousal classification accuracy from 61% to 78%, which mirrored the full range of classification accuracy across studies within the existing literature. Finally, we validated the predictive fidelity of our image-related brain states according to an independent measurement, autonomic arousal"
Quotes
  • "Here, we used pattern classification and hierarchical clustering to characterize the organization of a wide array of emotion categories in the human brain. We induced 14 emotions (6 ‘basic’, e.g. fear and anger; and 8 ‘non-basic’, e.g. shame and gratitude) and a neutral state using guided mental imagery while participants' brain activity was measured"
  • "Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that different basic and non-basic emotions have distinguishable neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits. "