ArticleFrontiers in neurorobotics2017
Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination.
Article in Frontiers in neurorobotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.Scientific reports · 2025Article
- EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach.Scientific reports · 2024Article
- Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram.Frontiers in neuroscience · 2024Article
- A novel feature fusion network for multimodal emotion recognition from EEG and eye movement signals.Frontiers in neuroscience · 2023Article
- Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition.Sensors (Basel, Switzerland) · 2022Article
- Understanding action concepts from videos and brain activity through subjects' consensus.Scientific reports · 2022Article
- Emotion recognition based on multi-modal physiological signals and transfer learning.Frontiers in neuroscience · 2022Article
- Affection of facial artifacts caused by micro-expressions on electroencephalography signals.Frontiers in neuroscience · 2022Article
- Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis.PloS one · 2022Article
- Microstate Detection in Naturalistic Electroencephalography Data: A Systematic Comparison of Topographical Clustering Strategies on an Emotional Database.Frontiers in neuroscience · 2022Article
- Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals.Entropy (Basel, Switzerland) · 2021Article
- Predicting Exact Valence and Arousal Values from EEG.Sensors (Basel, Switzerland) · 2021Article
- Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.Frontiers in computational neuroscience · 2021Article
- Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization.Frontiers in neuroscience · 2021Article
- A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.Frontiers in neuroscience · 2021Review
- Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.Sensors (Basel, Switzerland) · 2020Review
- Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection.Sensors (Basel, Switzerland) · 2020Article
- Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition.Sensors (Basel, Switzerland) · 2020Article
- Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network.Frontiers in human neuroscience · 2020Article
Corrections and comments
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing multiple-session EEG data as training sets. To this end, we developed a new EEG feature selection approach, transfer recursive feature elimination (T-RFE), to determine a set of the most robust EEG indicators with stable geometrical distribution across a group of training subjects and a specific testing subject. A validating set is introduced to independently determine the optimal hyper-parameter and the feature ranking of the T-RFE model aiming at controlling the overfitting. The effectiveness of the T-RFE algorithm for such cross-subject emotion classification paradigm has been validated by DEAP database. With a linear least square support vector machine classifier implemented, the performance of the T-RFE is compared against several conventional feature selection schemes and the statistical significant improvement has been found. The classification rate and
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.