ArticleSensors (Basel, Switzerland)2019
Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Personalized Classification of Scenario-Derived Operational Driver-State Classes from Non-Intrusive Wearable Signals in Real-World SAE Level 2 Automated Driving.Sensors (Basel, Switzerland) · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study.medRxiv : the preprint server for health sciences · 2025Article
- Tuning into flavor: predicting coffee sensory attributes from EEG with boosted-tree regression models.Frontiers in human neuroscience · 2025Article
- Identifying relevant EEG channels for subject-independent emotion recognition using attention network layers.Frontiers in psychiatry · 2025Article
- Identifying relevant asymmetry features of EEG for emotion processing.Frontiers in psychology · 2023Article
- The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models.Sensors (Basel, Switzerland) · 2021Article
- Upper Limb Rehabilitation Tools in Virtual Reality Based on Haptic and 3D Spatial Recognition Analysis: A Pilot Study.Sensors (Basel, Switzerland) · 2021Article
- Emotion and Stress Recognition Related Sensors and Machine Learning Technologies.Sensors (Basel, Switzerland) · 2021Article
- An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations.Sensors (Basel, Switzerland) · 2021Article
- Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization.Frontiers in neuroscience · 2021Article
- Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.PloS one · 2021Article
- Article
- Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.Sensors (Basel, Switzerland) · 2020Review
- CNN and LSTM-Based Emotion Charting Using Physiological Signals.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject's influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject's influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.
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Registered trials
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