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Emotions Classification Using EEG in Health Care

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

Online monitoring of mental well-being and factors contributing to it is vital, especially in pandemics (like COVID-19), when physical contact is discouraged. As emotions are closely connected to mental health, the monitoring and classification of emotions are also vital. In this paper, we present an emotion recognition framework recognizing emotions (angry, happiness, neutral, sadness, and scare) from neurological signals. Subjects can monitor their own emotions; the recognition outcome can also be transferred to healthcare professionals online for further investigation. For this, we use the brain’s electrical responses, containing the affective state of mind. These electrical responses or brain signals are recorded with electroencephalography (EEG) sensors. The EEG sensors are well established in their utility in non-medical and medical applications, such as biometrics or epilepsy detection. Here, we extract features from the EEG sensors and feed them as input to support vector machine (SVM) and random forest (RF) classifiers. Recognition rates of 76.32% and 79.18% have been recorded with SVM and RF classifiers, respectively. These results, along with the results attained using prior methods, demonstrate the efficacy of the proposed method.

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Notes

  1. 1.

    https://www.emotiv.com/epoc/.

  2. 2.

    https://en.wikipedia.org/wiki/European_Data_Format.

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Author information

Authors and Affiliations

  1. Machine Learning Group, EISLAB, Luleå Tekniska Universitet, Luleå, Sweden

    Sumit Rakesh, Foteini Liwicki, Hamam Mokayed, Richa Upadhyay, Prakash Chandra Chhipa, Vibha Gupta, Kanjar De, György Kovács & Rajkumar Saini

  2. Computer Science & Engineering, DCRUST, Murthal, Sonepat, India

    Dinesh Singh

  3. Department of CSE, IIT Roorkee, Roorkee, India

    Rajkumar Saini

Authors
  1. Sumit Rakesh
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  2. Foteini Liwicki
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  3. Hamam Mokayed
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  4. Richa Upadhyay
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  5. Prakash Chandra Chhipa
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  6. Vibha Gupta
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  7. Kanjar De
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  8. György Kovács
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  9. Dinesh Singh
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  10. Rajkumar Saini
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Corresponding author

Correspondence to Vibha Gupta .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rakesh, S. et al. (2023). Emotions Classification Using EEG in Health Care. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_4

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