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Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination

https://doi.org/10.3389/fnbot.2017.00019
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.3389/fnins.2020.546656openalex-snapshot
Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review
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Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization
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A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface
inferred← citeshttps://doi.org/10.3389/fnins.2022.1000716openalex-snapshot
Emotion recognition based on multi-modal physiological signals and transfer learning
inferred← citeshttps://doi.org/10.3389/fnins.2022.1048199openalex-snapshot
Affection of facial artifacts caused by micro-expressions on electroencephalography signals
inferred← citeshttps://doi.org/10.3389/fnins.2022.812624openalex-snapshot
Microstate Detection in Naturalistic Electroencephalography Data: A Systematic Comparison of Topographical Clustering Strategies on an Emotional Database
inferred← citeshttps://doi.org/10.3389/fnins.2023.1234162openalex-snapshot
A novel feature fusion network for multimodal emotion recognition from EEG and eye movement signals
inferred← citeshttps://doi.org/10.3389/fnins.2024.1355512openalex-snapshot
Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram
inferred← citeshttps://doi.org/10.3389/fphy.2020.629620openalex-snapshot
Differential Entropy Feature Signal Extraction Based on Activation Mode and Its Recognition in Convolutional Gated Recurrent Unit Network
inferred← citeshttps://doi.org/10.3389/frvir.2022.964754openalex-snapshot
Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals
inferred← citeshttps://doi.org/10.3390/a18120778openalex-snapshot
EEG-Based Decoding of Neural Mechanisms Underlying Impersonal Pronoun Resolution
inferred← citeshttps://doi.org/10.3390/app11031338openalex-snapshot
Multidimensional Emotion Recognition Based on Semantic Analysis of Biomedical EEG Signal for Knowledge Discovery in Psychological Healthcare
inferred← citeshttps://doi.org/10.3390/app7101060openalex-snapshot
Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks
inferred← citeshttps://doi.org/10.3390/books978-3-03897-395-9openalex-snapshot
Assistance Robotics and Biosensors
inferred← citeshttps://doi.org/10.3390/brainsci9110326openalex-snapshot
EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model
inferred← citeshttps://doi.org/10.3390/e23060642openalex-snapshot
Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals
inferred← citeshttps://doi.org/10.3390/math10111875openalex-snapshot
Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment
inferred← citeshttps://doi.org/10.3390/s17051014openalex-snapshot
A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition
inferred← citeshttps://doi.org/10.3390/s18030869openalex-snapshot
A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors
inferred← citeshttps://doi.org/10.3390/s18051339openalex-snapshot
Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks
inferred← citeshttps://doi.org/10.3390/s18051383openalex-snapshot
Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System
inferred← citeshttps://doi.org/10.3390/s19092212openalex-snapshot
Emotion Recognition from Multiband EEG Signals Using CapsNet
inferred← citeshttps://doi.org/10.3390/s20072034openalex-snapshot
Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition
inferred← citeshttps://doi.org/10.3390/s20113028openalex-snapshot
Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection
inferred← citeshttps://doi.org/10.3390/s20216321openalex-snapshot
Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review
inferred← citeshttps://doi.org/10.3390/s21103414openalex-snapshot
Predicting Exact Valence and Arousal Values from EEG
inferred← citeshttps://doi.org/10.3390/s22239282openalex-snapshot
Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition
inferred← citeshttps://doi.org/10.3390/sym11050683openalex-snapshot
Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals
inferred← citeshttps://doi.org/10.3390/sym16020241openalex-snapshot
Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies
inferred← citeshttps://doi.org/10.36548/jaicn.2022.2.003openalex-snapshot
Comparative analysis on Emotion Recognition by Multi-Channel CapsNet Learning Framework
inferred← citeshttps://doi.org/10.4018/ijcini.371401openalex-snapshot
A Hypernetwork-Based Feature Selection Method for EEG Classification
inferred← citeshttps://doi.org/10.48550/arxiv.1908.05349openalex-snapshot
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
inferred← citeshttps://doi.org/10.5120/ijca2019918896openalex-snapshot
Estimating Range and Relationship of EEG Frequency Bands for Emotion Recognition
inferred← citeshttps://doi.org/10.5204/thesis.eprints.129875openalex-snapshot
Emotion Classification Using Advanced Machine Learning Techniques Applied to Wearable Physiological Signals Data
inferred← citeshttps://doi.org/10.61797/ijbic.v2i2.243openalex-snapshot
Emotion Recognition from Electroencephalogram Signals based on Deep Neural Networks
inferred← citeshttps://doi.org/10.70675/1a2382fazc9e7z48cfza02aza081b4edd2e4openalex-snapshot
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