Abstract
In affective computing, emotion classification has a significant role and renders many applications in the areas like neuroscience, entertainment, neuro-marketing, and education. These applications comprise classification of neurological disorder, false detection, recognition of stress and pain level, and finding level of attention. The traditional methods used for emotion recognition are facial expressions or voice tone. However, the outcomes of facial signs and verbal language can indicate the unfair and unclear results. Hence, investigators have started the usage of EEG (Encephalogram) method for analyzing the brain signals to recognize the various emotions. EEG-based emotion recognition has an ability to modify the way that we detect some health disorders. Brain signals reveal variations in electrical potential as a result of communication among thousands of neurons. This research article includes analysis of human affective state using DEAP- “Dataset for Emotion Analysis using Physiological Signals”. It is a multimodal dataset, where 40 channels are used, 32 subjects participated, and 40 one-minute video pieces of music was shown to them. The participants evaluated each music video with respect to Valence, Arousal, Dominance, and Likings.
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Kulkarni, S., Patil, P.R. (2021). Analysis of DEAP Dataset for Emotion Recognition. In: Bhattacharyya, S., Nayak, J., Prakash, K.B., Naik, B., Abraham, A. (eds) International Conference on Intelligent and Smart Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 1312. Springer, Singapore. https://doi.org/10.1007/978-981-33-6176-8_8
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DOI: https://doi.org/10.1007/978-981-33-6176-8_8
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