AIMC Topic: Evoked Potentials, Visual
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Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.
IEEE journal of biomedical and health informatics
Apr 4, 2025
Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning technique...
Enhancing detection of SSVEPs using discriminant compacted network.
Journal of neural engineering
Feb 14, 2025
. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a c...
Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification perform...
Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.
Scientific reports
Jan 2, 2025
Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer int...
A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.
Sensors (Basel, Switzerland)
Dec 30, 2024
In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory...
Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.
Medical & biological engineering & computing
Dec 27, 2024
This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance...
Attention-Based Multimodal tCNN for Classification of Steady-State Visual Evoked Potentials and Its Application to Gripper Control.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
The classification problem for short time-window steady-state visual evoked potentials (SSVEPs) is important in practical applications because shorter time-window often means faster response speed. By combining the advantages of the local feature lea...
Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.
Journal of neural engineering
Nov 5, 2024
The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoisin...
Benchmarking brain-computer interface algorithms: Riemannian approaches vs convolutional neural networks.
Journal of neural engineering
Aug 21, 2024
To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI ...
The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 19, 2024
In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the exist...
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