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A System for the Study of Emotions with EEG Signals Using Machine Learning and Deep Learning

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Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1040))

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Abstract

Human life deals with a lot of emotions. Analyzing the emotions using EEG signals plays a pivotal role in determining the internal/inner state of a particular human. EEG deals with the spontaneous electrical activity of neurons as recorded from multiple electrodes placed in the interior region of the brain. Initially, EEG signals are captured and preprocessed for the removal of noise signals. Selection of appropriate classification techniques in emotion analysis is an important task. The classifiers like k-nearest neighbor (k-NN), SVM, LDA were evaluated. Performances of the classifiers in analyzing a wide range of emotions (arousal and valence emotions) were examined. The results examined demonstrated that emotion analysis using EEG signals is highly advantageous and efficient than the existing traditional recognition systems.

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Author information

Authors and Affiliations

  1. SASTRA Deemed University, Tirumalaisamudram, Thanjavur, Tamil Nadu, India

    Vasupalli Jaswanth & J. Naren

Authors
  1. Vasupalli Jaswanth
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  2. J. Naren
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Corresponding author

Correspondence to J. Naren .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, India

    Akash Kumar Bhoi

  4. Division of Information and Communication, Baekseok University, Cheonan-si, Ch’ungch’ong-namdo, Korea (Republic of)

    Gyoo-Soo Chae

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Jaswanth, V., Naren, J. (2020). A System for the Study of Emotions with EEG Signals Using Machine Learning and Deep Learning. In: Mallick, P., Balas, V., Bhoi, A., Chae, GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-15-1451-7_7

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