Impact of eeg parameters detecting dementia diseases: A systematic review
LM Sánchez-Reyes, J Rodríguez-Reséndiz… - IEEE …, 2021 - ieeexplore.ieee.org
Dementia diseases are increasing rapidly, according to the World Health Organization
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …
Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction
Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
NeuroGrasp: Real-time EEG classification of high-level motor imagery tasks using a dual-stage deep learning framework
Brain–computer interfaces (BCIs) have been widely employed to identify and estimate a
user's intention to trigger a robotic device by decoding motor imagery (MI) from an …
user's intention to trigger a robotic device by decoding motor imagery (MI) from an …
CNN based classification of motor imaginary using variational mode decomposed EEG-spectrum image
K Keerthi Krishnan, KP Soman - Biomedical engineering letters, 2021 - Springer
A novel approach of preprocessing EEG signals by generating spectrum image for effective
Convolutional Neural Network (CNN) based classification for Motor Imaginary (MI) …
Convolutional Neural Network (CNN) based classification for Motor Imaginary (MI) …
Granger causal inference based on dual laplacian distribution and its application to MI-BCI classification
Granger causality-based effective brain connectivity provides a powerful tool to probe the
neural mechanism for information processing and the potential features for brain computer …
neural mechanism for information processing and the potential features for brain computer …
Online adaptation boosts SSVEP-based BCI performance
Objective: A user-friendly steady-state visual evoked potential (SSVEP)-based brain-
computer interface (BCI) prefers no calibration for its target recognition algorithm, however …
computer interface (BCI) prefers no calibration for its target recognition algorithm, however …
Real-time deep neurolinguistic learning enhances noninvasive neural language decoding for brain–machine interaction
Electroencephalogram (EEG)-based brain–machine interface (BMI) has been utilized to
help patients regain motor function and has recently been validated for its use in healthy …
help patients regain motor function and has recently been validated for its use in healthy …
Multiscale time-frequency method for multiclass motor imagery brain computer interface
G Liu, L Tian, W Zhou - Computers in Biology and Medicine, 2022 - Elsevier
Abstract Motor Imagery Brain Computer Interface (MI-BCI) has become a promising
technology in the field of neurorehabilitation. However, the performance and computational …
technology in the field of neurorehabilitation. However, the performance and computational …
Transferring subject-specific knowledge across stimulus frequencies in SSVEP-based BCIs
Learning from subject's calibration data can significantly improve the performance of a
steady-state visually evoked potential (SSVEP)-based brain–computer interface (BCI), for …
steady-state visually evoked potential (SSVEP)-based brain–computer interface (BCI), for …
Aggregating from multiple target-shifted sources
Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for
predicting a related target domain. Hence, a crucial aspect is to properly combine different …
predicting a related target domain. Hence, a crucial aspect is to properly combine different …