ArticleScientific reports2021
Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Plant emotion revisited: toward a new conceptual framework.Protoplasma · 2026Review
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- Toward Objective Assessment of Positive Affect: EEG and HRV Indices Distinguishing High and Low Arousal Positive Affect.Sensors (Basel, Switzerland) · 2026Article
- EEG-based emotion recognition using phase-space reconstruction with Poincaré sections: a study on the AMIGOS dataset.Frontiers in human neuroscience · 2026Article
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- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Emotion Recognition Using Temporal Facial Skin Temperature and Eye-Opening Degree During Digital Content Viewing for Japanese Older Adults.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Eye Tracker Integrated with Cylindrical Capacitive Sensors for Chronic Fatigue Assessment.Advanced sensor research · 2025Article
- Deep-Learning-Based Analysis of Electronic Skin Sensing Data.Sensors (Basel, Switzerland) · 2025Review
- RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition.Frontiers in neuroscience · 2025Article
- Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.Brain sciences · 2024Article
- E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring.Biomedicines · 2024Review
- Space-CNN: a decision classification method based on EEG signals from different brain regions.Medical & biological engineering & computing · 2024Article
- Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface.Nature communications · 2024Article
- Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review.Sensors (Basel, Switzerland) · 2023Review
- Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review.Biosensors · 2023Review
- Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application.Sensors (Basel, Switzerland) · 2023Article
- Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2022Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of this study was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. Moreover, we propose identifying epochs, which are the instants at which excitation is maximum, during the emotion to improve the system's accuracy. We used the zero-time windowing method to extract instantaneous spectral information using the numerator group-delay function to accurately detect the epochs in each emotional state. Different classification scheme were defined using QDC and RNN and evaluated using the DEAP database. The experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. The average accuracy rate exceeded 89%. Compared with existing algorithms dealing with 9 emotions, the proposed method enhanced the accuracy rate by 8%. Moreover, experiment shows that the proposed system outperforms similar approaches discriminating between 3 and 4 emotions only. We also found that the proposed method works well, even when applying conventional classification algorithms.
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