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

Relationship Formation02:12

Relationship Formation

46.2K
What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
46.2K
Ending Relationships01:28

Ending Relationships

192
The dissolution of intimate relationships presents complex emotional and psychological challenges, particularly when emotional bonds are strong, the relationship is long-standing, and perceived alternatives are limited. This distress often intensifies in romantic breakups, where the initiator may experience greater turmoil than the rejected partner. Contributing factors include residual attachment, guilt over causing pain, and uncertainty about how to manage the situation. The stress is further...
192
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Neural Regulation01:37

Neural Regulation

43.4K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
43.4K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.1K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.1K

You might also read

Related Articles

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

Sort by
Same author

Domain generalized feature embedded learning for calibration-free event-related potentials recognition.

Cognitive neurodynamics·2026
Same author

Sum of similarity-regularized squared correlations for enhancing SSVEP detection.

Artificial intelligence in medicine·2025
Same author

Motion Cognitive Decoding of Cross-Subject Motor Imagery Guided on Different Visual Stimulus Materials.

Journal of integrative neuroscience·2024
Same author

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification.

Frontiers in neuroscience·2023
Same author

Cross-subject aesthetic preference recognition of Chinese dance posture using EEG.

Cognitive neurodynamics·2023
Same author

EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss.

Frontiers in neuroinformatics·2020
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Feb 4, 2026

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.8K

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.

Tian-Jian Luo1, Chang-le Zhou1, Fei Chao2,3

  • 1Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005, China.

BMC Bioinformatics
|October 1, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep recurrent neural network (RNN) approach for motor imagery brain-computer interfaces (MI-BCIs). The new method significantly improves classification accuracy using spatial-frequency-sequential features extracted from limited EEG data.

Keywords:
Brain computer interfaceDeep recurrent neural networksEEG signals classificationSpatial-frequency-sequential relationships

More Related Videos

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

11.4K
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

2.1K

Related Experiment Videos

Last Updated: Feb 4, 2026

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.8K
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

11.4K
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

2.1K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Conventional motor imagery brain-computer interfaces (MI-BCIs) face performance limitations due to insufficient data and oversimplified features.
  • Existing spatial-frequency features and shallow classifiers yield suboptimal results in MI-BCI applications.

Purpose of the Study:

  • To develop a high-accuracy and robust MI-BCI system using deep learning on limited EEG signal samples.
  • To explore the efficacy of spatial-frequency-sequential relationships for enhanced MI-BCI signal classification.

Main Methods:

  • A deep recurrent neural network (RNN) combined with a sliding window cropping strategy (SWCS) was employed for MI-BCI signal classification.
  • Spatial-frequency features were extracted using the filter bank common spatial pattern (FB-CSP) algorithm and then processed by the SWCS.
  • Gated recurrent unit (GRU) and long-short term memory (LSTM) units were investigated within the RNN architecture to address memory limitations.

Main Results:

  • The proposed spatial-frequency-sequential relationships demonstrated superior performance compared to existing spatial-frequency methods on benchmark BCI datasets.
  • The GRU-RNN architecture achieved the lowest misclassification rates across all tested BCI benchmark datasets.

Conclusions:

  • The integration of spatial-frequency-sequential relationships and time-slice cropping offers a novel approach for developing accurate and robust MI-BCIs.
  • This method effectively models high-accuracy MI-BCIs even with limited electroencephalography (EEG) signal trials.