A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques

Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classif...

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Veröffentlicht in:	MethodsX Jg. 15; S. 103468
Hauptverfasser:	Hasan, Rabita, Islam, Sheikh Md. Rabiul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.12.2025 Elsevier
Schlagworte:	Boosted EEG Emotion Classification Using Differential Entropy and Higuchi's Fractal Dimension DEAP dataset EEG signals Emotion classification Feature extraction Machine learning Feature extraction EEG signals Boosted EEG Emotion Classification Using Differential Entropy and Higuchi's Fractal Dimension Emotion classification Machine learning DEAP dataset
ISSN:	2215-0161, 2215-0161
Online-Zugang:	Volltext
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