Emotion Recognition from EEG Signals: A Machine Learning Perspective Using DEAP Dataset

2026
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Authors: N. Arul Anand; Viraj Agarwal; R. Krithik; D. Kavya; S. Pranav; R. Keerthana Srija
Published in: Smart System for Integrated Computing and Communication
Publisher: Springer Nature Switzerland

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Abstract

Emotion recognition from EEG signals is a rapidly evolving field with significant implications for mental health, human-computer interaction, and market research. This chapter delves into the use of machine learning techniques to analyze EEG data and classify emotional states, focusing on the DEAP dataset. The text explores the principles of EEG, the types of brain waves, and the correlation between EEG signals and human emotions. It also discusses the challenges and advancements in using machine learning models for emotion recognition, highlighting the importance of specific brain regions and frequency bands. The chapter concludes with a detailed analysis of the results, showcasing the potential of EEG-based emotion recognition in practical applications. Readers will gain insights into the latest research and applications in this field, making this chapter a valuable resource for professionals looking to stay updated on the latest developments in emotion recognition.

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Title: Emotion Recognition from EEG Signals: A Machine Learning Perspective Using DEAP Dataset
Authors: N. Arul Anand
Viraj Agarwal
R. Krithik
D. Kavya
S. Pranav
R. Keerthana Srija
Publisher: Springer Nature Switzerland
Book: Smart System for Integrated Computing and Communication
Print ISBN: 978-3-031-99938-3

Electronic ISBN: 978-3-031-99939-0

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-031-99939-0_23

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