ArticleSensors (Basel, Switzerland)2019
A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition.
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 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 100 citations in OpenAlex.
- Fear-EEG-AI: A Conceptual and Operational Framework for Designing EEG-AI Systems for the Inference of Fear-Compatible Neurophysiological States.Sensors (Basel, Switzerland) · 2026Article
- Arrhythmia Classification with Single-Channel Features Extracted from "A Large-Scale 12-Lead ECG Database for Arrhythmia Study".Sensors (Basel, Switzerland) · 2025Article
- MB-MSTFNet: A Multi-Band Spatio-Temporal Attention Network for EEG Sensor-Based Emotion Recognition.Sensors (Basel, Switzerland) · 2025Article
- Distributed Sparse Manifold-Constrained Optimization Algorithm in Linear Discriminant Analysis.Journal of imaging · 2025Article
- Noninvasive brain stimulation during EEG improves machine learning classiResearch square · 2024Article
- A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.Brain informatics · 2024Review
- Research on Fatigue Driving Detection Technology Based on CA-ACGAN.Brain sciences · 2024Article
- MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition.Brain sciences · 2024Article
- Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier.Brain informatics · 2024Article
- Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology.Frontiers in neuroscience · 2024Article
- A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.Frontiers in physiology · 2024Article
- Emotional State Classification from MUSIC-Based Features of Multichannel EEG Signals.Bioengineering (Basel, Switzerland) · 2023Article
- Attention-based 3D convolutional recurrent neural network model for multimodal emotion recognition.Frontiers in neuroscience · 2023Article
- Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals.Diagnostics (Basel, Switzerland) · 2022Article
- Emotion Recognition from Physiological Channels Using Graph Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Exploring EEG Characteristics to Identify Emotional Reactions under Videogame Scenarios.Brain sciences · 2021Article
- Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization.Frontiers in neuroscience · 2021Article
- Like/Dislike Prediction for Sport Shoes With Electroencephalography: An Application of Neuromarketing.Frontiers in human neuroscience · 2021Article
- Positive and Negative Emotion Classification Based on Multi-channel.Frontiers in behavioral neuroscience · 2021Article
- Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.