Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Rich-club organization and functional brain network metrics during facial emotional processing: a task-based fMRI study.

Behavioral and brain functions : BBF·2026

Same author

Spike dynamics in primate lateral prefrontal cortex during working memory and decision-making: A fractal analysis.

iScience·2026

Same author

Electroencephalography during acute painful procedures in neonates: a scoping review.

Pain reports·2026

Same author

Genetic control of dynamic brain network reconfiguration during working memory.

PloS one·2026

Same author

Sensory modality shapes post-learning functional connectivity.

iScience·2026

Same author

Neural mechanisms of cost-benefit decision-making in the rodent anterior cingulate cortex: A systematic review of extracellular recordings.

Physiology & behavior·2025

Same journal

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.

Neuroinformatics·2026

Same journal

CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.

Neuroinformatics·2026

Same journal

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Neuroinformatics·2026

Same journal

HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of Dynamic Functional Connectivity.

Neuroinformatics·2026

Same journal

Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.

Neuroinformatics·2026

Same journal

Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.

Neuroinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 27, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Regularized Partial Least Square Regression for Continuous Decoding in Brain-Computer Interfaces.

Reza Foodeh¹, Saeed Ebadollahi², Mohammad Reza Daliri³

¹Neuroscience and Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, Tehran, 16846-13114, Iran.

Neuroinformatics

|February 29, 2020

Summary

This summary is machine-generated.

A new regularized partial least square (RPLS) method improves brain-computer interface (BCI) decoding by reducing overfitting and enhancing robustness against noise, outperforming existing techniques in various BCI and spectroscopy applications.

Keywords:

Brain computer interface (BCI)Continuous decoding Estimation Partial least square (PLS)Regularization Sparsity

More Related Videos

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

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

Published on: September 8, 2023

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

Related Experiment Videos

Last Updated: Dec 27, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

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

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

Published on: September 8, 2023

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

Area of Science:

Computational neuroscience
Machine learning for biosignal processing
Biomedical engineering

Background:

Continuous decoding is essential for brain-computer interfaces (BCIs).
Linear regression methods, including Partial Least Square (PLS), are common but prone to overfitting.
Existing methods struggle with noise and generalization in BCI data.

Purpose of the Study:

To introduce a novel Regularized Partial Least Square (RPLS) method for improved decoding in BCIs.
To enhance the robustness and accuracy of decoding algorithms against overfitting and noise.
To demonstrate the versatility of RPLS across different BCI datasets and non-BCI applications.

Main Methods:

Developed RPLS by incorporating regularized least squares into the PLS framework.
Evaluated RPLS against Ridge Regression (RR), PLS, and PLS with regularized weights (PLSRW) on ECoG and LFP BCI datasets.
Assessed noise resistance using a semi-simulated BCI dataset and tested generalization on near-infrared (NIR) spectroscopy data.

Main Results:

RPLS significantly outperformed RR, PLS, and PLSRW in decoding accuracy on two real BCI datasets.
RPLS demonstrated superior robustness against increasing numbers of latent variables compared to PLS and PLSRW.
RPLS maintained higher performance across all noise levels and showed superior decoding in NIR spectroscopy data.

Conclusions:

RPLS offers a more accurate and robust decoding solution for BCI applications compared to existing methods.
The proposed method effectively mitigates overfitting and enhances generalization capabilities.
RPLS shows broad applicability beyond BCIs, including chemical spectroscopy data analysis.