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

Anthocyanin-based colorimetric sensor for aflatoxin B1 (AFB1) detection in wheat: Performance optimization via near-infrared (NIR) spectral characterization and structurally improved recurrent neural network (RNN).

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

Same author

MPAN: A Multi-Prototype Adaptive Network for Few-Shot EEG-Based Biometric Recognition.

IEEE journal of biomedical and health informatics·2026

Same author

A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding.

Brain sciences·2026

Same author

BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.

IEEE journal of biomedical and health informatics·2026

Same author

LAE-Net: Large Pretrained Models Assistant Text-Guided Image Editing Adversarial Network.

IEEE transactions on visualization and computer graphics·2026

Same author

Adaptive graph convolution domain adaptation network for cross-subject EEG emotion recognition.

Medical & biological engineering & computing·2026

Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026

Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026

Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026

Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

Computer methods and programs in biomedicine·2026

Same journal

Leaflet resection improves valve expansion and hemodynamic performance in redo TAVI with balloon- and self-expanding transcatheter heart valve configurations.

Computer methods and programs in biomedicine·2026

Same journal

Spectral super-resolution for Parkinson's voice via representation-level methods under mixed-reality acquisition.

Computer methods and programs in biomedicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 4, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Sub-band target alignment common spatial pattern in brain-computer interface.

Xianxiong Zhang¹, Qingshan She¹, Yun Chen¹

¹School of Automation, Hangzhou DianZi University, Hangzhou 310018, China.

Computer Methods and Programs in Biomedicine

|May 25, 2021

Summary

This summary is machine-generated.

This study introduces a new brain-computer interface method, sub-band target alignment common spatial pattern (SBTACSP), to improve cross-subject motor imagery classification accuracy by combining sub-band filtering and transfer learning.

Keywords:

Brain-computer interface Cross-subject classification Sub-band filtering Target alignment Transfer learning

More Related Videos

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Related Experiment Videos

Last Updated: Nov 4, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Area of Science:

Brain-Computer Interfaces (BCI)
Neuroscience
Machine Learning

Background:

Sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) enhance BCI classification accuracy by selecting specific frequency bands.
Cross-subject classification in BCIs is limited by individual differences.

Purpose of the Study:

To introduce and evaluate the sub-band target alignment common spatial pattern (SBTACSP) method for cross-subject classification of motor imagery (MI) electroencephalography (EEG) signals.
To leverage transfer learning to overcome individual differences in cross-subject BCI.

Main Methods:

EEG signals were filtered into multiple frequency bands (sub-band filtering).
Source domain data was aligned to the target domain space within each frequency band.
Common spatial pattern (CSP) algorithm extracted features, with minimum redundancy maximum relevance (mRMR) selecting representative features.
Features from all sub-bands were fused and classified using linear discriminant analysis (LDA).

Main Results:

The SBTACSP method achieved the best performance among six state-of-the-art algorithms.
Mean classification accuracies of 75.15% (Dataset Ⅱa) and 66.85% (Dataset Ⅱb) were obtained in cross-subject classification.

Conclusions:

Combining sub-band filtering and transfer learning yields superior classification performance.
The proposed SBTACSP algorithm significantly advances the practical application of MI-based BCIs.