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June 18, 2026International Journal of Medical Engineering and Informatics

Human emotion classification enabled by EEG signal analysis and machine learning

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Authors

DTDattaprasad A. TorseMBMahadev M. Bagade

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Overview

Randomized trial demonstrates accurate emotion recognition through EEG signals, implying advancements in automated systems.

Key Points

  • The aim is to enhance automated human emotion recognition using EEG data and machine learning techniques.
  • Utilized the DEAP dataset with EEG data from 32 participants and the EMOTIV Insight headset.
  • Applied tunable-Q wavelet transform (TQWT) for frequency domain feature extraction.
  • Classified data using K-nearest neighbour (KNN) and random forest (RF) algorithms.
  • Achieved classification accuracy of 97.8% using KNN and RF.
  • Utilized a continuous valence-arousal-dominance framework for emotion representation.

Cite This Study

Torse et al. (2026) studied this question.

synapsesocial.com/papers/6a338db6630953a74978e8fchttps://doi.org/10.1504/ijmei.2026.154134
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  1. 1Efficient approach for EEG‐based emotion recognition2020 · 38 citations
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  4. 4Emotion detection using EEG: hybrid classification approach2024 · 1 citations
  5. 5Human Emotion Classification Based on EEG Signals Using Naïve Bayes Method2019 · 30 citations