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Emotion Recognition Through Physiological Signals and Brain Sensing

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Inventive Communication and Computational Technologies (ICICCT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 757))

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Abstract

Throughout many distresses, more emotional discriminative detail in attributes has been the decisive element in increasing classification in electroencephalograph (EEG) emotion identification studies. This paper offers an emotion recognition approach that can successfully extract spatiotemporal information associated to individual’s emotion-temperament. Our major purpose is to recognize other emotional distress like anxiousness, tension, and aggression. Adding a 2-layer LSTM module, it can make up for the lack of research on EEG signal channel correlations. Finally, for emotion classification, integrated characteristics are feed into a SoftMax classifier through a fully connected neural network layer. To extract the features from multi-channel original EEG data, convolution was employed to one channel at a time, and Long Short-Term Memory a recurrent neural network was used to search for correlations among these features. Finally, the classification results are generated using a Softmax classifier Also, stress bins were created for each user to detect his level of stress or calm after being exposed to external stimuli.

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Notes

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Authors and Affiliations

  1. Department of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India

    Disha Shah & Rashmi Rane

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  1. Disha Shah
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  2. Rashmi Rane
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Correspondence to Rashmi Rane .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Department of Computer Science (HUMAIN-Lab), International Hellenic University, Thessaloniki, Greece

    George A. Papakostas

  3. Information Systems and Operations Management (ISEG), University of Lisbon, Lisboa, Portugal

    Álvaro Rocha

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Shah, D., Rane, R. (2023). Emotion Recognition Through Physiological Signals and Brain Sensing. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_55

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