A Systematic Review of Using Deep Learning Technology in the Steady‐State Visually Evoked Potential‐Based Brain‐Computer Interface Applications: Current …

AS Albahri, ZT Al-Qaysi, L Alzubaidi… - … of Telemedicine and …, 2023 - Wiley Online Library
The significance of deep learning techniques in relation to steady‐state visually evoked
potential‐(SSVEP‐) based brain‐computer interface (BCI) applications is assessed through …

Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

L Xu, M Xu, TP Jung, D Ming - Cognitive neurodynamics, 2021 - Springer
A brain–computer interface (BCI) can connect humans and machines directly and has
achieved successful applications in the past few decades. Many new BCI paradigms and …

Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface

AM Roy - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
Abstract Objective. Deep learning (DL)-based brain–computer interface (BCI) in motor
imagery (MI) has emerged as a powerful method for establishing direct communication …

An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

AM Roy - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …

A comprehensive review of deep learning power in steady-state visual evoked potentials

ZT Al-Qaysi, AS Albahri, MA Ahmed, RA Hamid… - Neural Computing and …, 2024 - Springer
Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from
diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI …

TRCA-net: using TRCA filters to boost the SSVEP classification with convolutional neural network

Y Deng, Q Sun, C Wang, Y Wang… - Journal of Neural …, 2023 - iopscience.iop.org
Objective. The steady-state visual evoked potential (SSVEP)-based brain–computer
interface has received extensive attention in research due to its simple system, less training …

A multi-scale fusion CNN model based on adaptive transfer learning for multi-class MI-classification in BCI system

AM Roy - BioRxiv, 2022 - biorxiv.org
Deep learning-based brain-computer interface (BCI) in motor imagery (MI) has emerged as
a powerful method for establishing direct communication between the brain and external …

Classification of image encoded SSVEP-based EEG signals using Convolutional Neural Networks

PO De Paula, TB da Silva Costa… - Expert Systems with …, 2023 - Elsevier
Abstract Brain–Computer Interfaces (BCI) systems based on electroencephalography (EEG)
signals are experiencing a rapid development, counting with a number of methods, mainly …

Fixed template network and dynamic template network: novel network designs for decoding steady-state visual evoked potentials

X Xiao, L Xu, J Yue, B Pan, M Xu… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Decomposition methods are efficient to decode steady-state visual evoked
potentials (SSVEPs). In recent years, the brain–computer interface community has also been …

A survey of deep learning-based classification methods for steady-state visual evoked potentials

Y Pan, J Chen, Y Zhang - Brain-Apparatus Communication: A …, 2023 - Taylor & Francis
Purpose Steady-state visual evoked potential (SSVEP) based BCI has attracted great
interests owing to the high information transfer rate (ITR) and little training requirement. The …