A Systematic Review of Using Deep Learning Technology in the Steady‐State Visually Evoked Potential‐Based Brain‐Computer Interface Applications: Current …
The significance of deep learning techniques in relation to steady‐state visually evoked
potential‐(SSVEP‐) based brain‐computer interface (BCI) applications is assessed through …
potential‐(SSVEP‐) based brain‐computer interface (BCI) applications is assessed through …
A comprehensive review of deep learning power in steady-state visual evoked potentials
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 …
diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI …
A simplified CNN classification method for MI-EEG via the electrode pairs signals
X Lun, Z Yu, T Chen, F Wang, Y Hou - Frontiers in Human …, 2020 - frontiersin.org
A brain-computer interface (BCI) based on electroencephalography (EEG) can provide
independent information exchange and control channels for the brain and the outside world …
independent information exchange and control channels for the brain and the outside world …
Comparing user-dependent and user-independent training of CNN for SSVEP BCI
Objective. We presented a comparative study on the training methodologies of a
convolutional neural network (CNN) for the detection of steady-state visually evoked …
convolutional neural network (CNN) for the detection of steady-state visually evoked …
Establishment and application of intelligent city building information model based on BP neural network model
YW Li, K Cao - Computer Communications, 2020 - Elsevier
The construction of smart cities in our country has received extensive attention. Under the
situation that smart cities are vigorously promoted nowadays, compared with traditional …
situation that smart cities are vigorously promoted nowadays, compared with traditional …
EEGNet with ensemble learning to improve the cross-session classification of SSVEP based BCI from ear-EEG
Y Zhu, Y Li, J Lu, P Li - IEEE Access, 2021 - ieeexplore.ieee.org
Ear-electroencephalography (ear-EEG) using electrodes placed above hairless areas
around ears is a convenient and comfortable method for signal recording in practical …
around ears is a convenient and comfortable method for signal recording in practical …
Filter bank convolutional neural network for short time-window steady-state visual evoked potential classification
Convolutional neural network (CNN) has been gradually applied to steady-state visual
evoked potential (SSVEP) of the brain-computer interface (BCI). Frequency-domain features …
evoked potential (SSVEP) of the brain-computer interface (BCI). Frequency-domain features …
Convolutional neural networks ensemble model for neonatal seizure detection
Background Neonatal seizures are a common occurrence in clinical settings, requiring
immediate attention and detection. Previous studies have proposed using manual feature …
immediate attention and detection. Previous studies have proposed using manual feature …
Temporal-spatial-frequency depth extraction of brain-computer interface based on mental tasks
L Wang, W Huang, Z Yang, C Zhang - Biomedical Signal Processing and …, 2020 - Elsevier
With the help of brain-computer interface (BCI) systems, the electroencephalography (EEG)
signals can be translated into control commands. It is rare to extract temporal-spatial …
signals can be translated into control commands. It is rare to extract temporal-spatial …
Decoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysis
L Chang, R Wang, Y Zhang - Biomedical Signal Processing and Control, 2022 - Elsevier
Steady-state visual evoked potential (SSVEP) is one of the most popular neural patterns
used to develop brain-computer interface (BCI). To address the issue of …
used to develop brain-computer interface (BCI). To address the issue of …