Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition.

Sensors (Basel, Switzerland)·2019
Same author

Inhibition of STAT3 activation mediated by toll-like receptor 4 attenuates angiotensin II-induced renal fibrosis and dysfunction.

British journal of pharmacology·2019
Same author

Human red and green cone opsins are <i>O</i>-glycosylated at an N-terminal Ser/Thr-rich domain conserved in vertebrates.

The Journal of biological chemistry·2019
Same author

Improving Plant Genome Editing with High-Fidelity xCas9 and Non-canonical PAM-Targeting Cas9-NG.

Molecular plant·2019
Same author

Likelihood-Ratio-Test Methods for Drug Safety Signal Detection from Multiple Clinical Datasets.

Computational and mathematical methods in medicine·2019
Same author

Dual complementary liposomes inhibit triple-negative breast tumor progression and metastasis.

Science advances·2019
Same journal

Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.

Frontiers in neuroinformatics·2026
Same journal

Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

Frontiers in neuroinformatics·2026
Same journal

FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

Frontiers in neuroinformatics·2026
Same journal

A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.

Frontiers in neuroinformatics·2026
Same journal

IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.

Frontiers in neuroinformatics·2026
Same journal

Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

Frontiers in neuroinformatics·2026
See all related articles

Related Experiment Video

Updated: Dec 10, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

825

Sparse Logistic Regression With L 1/2 Penalty for Emotion Recognition in Electroencephalography Classification.

Dong-Wei Chen1, Rui Miao2, Zhao-Yong Deng1

  • 1School of Electronic Information Engineering, University of Electronic Science and Technology of China, Zhongshan, China.

Frontiers in Neuroinformatics
|August 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces L1/2 penalty logistic regression for electroencephalography (EEG) emotion recognition. This method enhances classification accuracy and reduces computational complexity by selecting more informative EEG signals.

Keywords:
EEGL1 regularizationL1/2 regularizationRidge Regressionemotion recognitionsparse logistic regression

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.8K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.9K

Related Experiment Videos

Last Updated: Dec 10, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

825
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.8K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.9K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Emotion recognition using electroencephalography (EEG) is crucial for brain-computer interfaces.
  • Classifying EEG data is challenging due to noise and large datasets.
  • Effective feature extraction is vital for accurate EEG signal processing.

Purpose of the Study:

  • To investigate the efficacy of L1/2 penalty in sparse logistic regression for three-classification EEG emotion recognition.
  • To compare L1/2 penalty with existing regularization methods like L1, Ridge Regression, and Elastic Net.
  • To demonstrate the benefits of L1/2 regularization for high-dimensional, small-sample EEG data.

Main Methods:

  • Implemented L1/2 penalty logistic regression using a coordinate descent algorithm.
  • Employed a univariate semi-threshold operator for L1/2 penalty logistic regression.
  • Evaluated the proposed method on both simulated and real EEG data.

Main Results:

  • The proposed L1/2 penalty logistic regression achieved higher classification accuracy compared to L1, Ridge Regression, and Elastic Net.
  • The method effectively extracts fewer, more informative EEG signals.
  • Demonstrated improved computational accuracy and reduced complexity.

Conclusions:

  • Sparse logistic regression with L1/2 penalty is an effective technique for EEG emotion recognition.
  • This method offers significant advantages for high-dimensional and small-sample EEG datasets.
  • The findings support the use of L1/2 penalty for practical emotion recognition applications.