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 …
2020 International brain–computer interface competition: A review
The brain-computer interface (BCI) has been investigated as a form of communication tool
between the brain and external devices. BCIs have been extended beyond communication …
between the brain and external devices. BCIs have been extended beyond communication …
Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks.
The COVID-19 pandemic has precipitated an unprecedented surge in the proliferation of
online E-learning platforms, designed to cater to a wide array of subjects across all age …
online E-learning platforms, designed to cater to a wide array of subjects across all age …
Calibration free meta learning based approach for subject independent EEG emotion recognition
Abstract Brain Computer Interfaces (BCI) detect changes in the electrical activity of brain
which could be applied in use-cases like environmental control, neuro-rehabilitation etc …
which could be applied in use-cases like environmental control, neuro-rehabilitation etc …
A multiple frequency bands parallel spatial–temporal 3D deep residual learning framework for EEG-based emotion recognition
M Miao, L Zheng, B Xu, Z Yang, W Hu - Biomedical Signal Processing and …, 2023 - Elsevier
Electroencephalography (EEG) based emotion recognition has become a hot research
issue in the field of cognitive interaction and brain-computer interface (BCI). How to build a …
issue in the field of cognitive interaction and brain-computer interface (BCI). How to build a …
Transfer learning with optimal transportation and frequency mixup for EEG-based motor imagery recognition
P Chen, H Wang, X Sun, H Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a
degenerate performance due to the considerable individual variability. To address this …
degenerate performance due to the considerable individual variability. To address this …
Discriminative adversarial network based on spatial-temporal-graph fusion for motor imagery recognition
Motor imagery (MI)-based electroencephalography (EEG) stands as a prominent paradigm
in the brain–computer interface (BCI) field, which is frequently applied in neural …
in the brain–computer interface (BCI) field, which is frequently applied in neural …
Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions
This paper presents a systematic literature review, providing a comprehensive taxonomy of
Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) …
Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) …
Learning a robust unified domain adaptation framework for cross-subject EEG-based emotion recognition
Over the last few years, unsupervised domain adaptation (UDA) based on deep learning
has emerged as a solution to build cross-subject emotion recognition models from …
has emerged as a solution to build cross-subject emotion recognition models from …
Studies to overcome brain–computer interface challenges
WS Choi, HG Yeom - Applied Sciences, 2022 - mdpi.com
A brain–computer interface (BCI) is a promising technology that can analyze brain signals
and control a robot or computer according to a user's intention. This paper introduces our …
and control a robot or computer according to a user's intention. This paper introduces our …