Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress
I Hameed, DM Khan, SM Ahmed, SS Aftab… - Computers in Biology …, 2025 - Elsevier
This systematic literature review explores the intersection of neuroscience and deep
learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to …
learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to …
Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework
Motor imagery brain-computer interfaces (MI-BCIs) have gained a lot of attention in recent
years thanks to their potential to enhance rehabilitation and control of prosthetic devices for …
years thanks to their potential to enhance rehabilitation and control of prosthetic devices for …
EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation
C Hua, J Tao, Z Zhou, L Chai, Y Yan, J Liu… - Computer Methods and …, 2024 - Elsevier
Background Virtual reality motion sickness (VRMS) is a key issue hindering the
development of virtual reality technology, and accurate detection of its occurrence is the first …
development of virtual reality technology, and accurate detection of its occurrence is the first …
Increasing Robustness of Intracortical Brain-Computer Interfaces for Recording Condition Changes via Data Augmentation
SH Yang, CJ Huang, JS Huang - Computer Methods and Programs in …, 2024 - Elsevier
ABSTRACT Background and Objective Intracortical brain-computer interfaces (iBCIs) aim to
help paralyzed individuals restore their motor functions by decoding neural activity into …
help paralyzed individuals restore their motor functions by decoding neural activity into …
A session-incremental broad learning system for motor imagery EEG classification
Y Yang, M Li, H Liu, Z Li - Biomedical Signal Processing and Control, 2024 - Elsevier
Motor imagery electroencephalogram (MI-EEG) plays an important role in the brain
computer interface-based neuro-rehabilitation, and it is challenging to make the recognition …
computer interface-based neuro-rehabilitation, and it is challenging to make the recognition …
An adaptive session-incremental broad learning system for continuous motor imagery EEG classification
Y Yang, M Li, L Wang - Medical & Biological Engineering & Computing, 2024 - Springer
Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in
neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is …
neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is …
Intelligent EEG Artifact Removal in Motor ImageryBCI: Synergizing FCIF, FCFBCSP, and Modified DNN with SNR, PSD, and Spectral Coherence Evaluation
S Akuthota, K Rajkumar… - … Conference on Circuit …, 2024 - ieeexplore.ieee.org
Motor Imagery Brain-Computer Interfaces (MI BCIs) face challenges due to artifacts in
electroencephalogram (EEG) signals, hindering accurate decoding of imagined movements …
electroencephalogram (EEG) signals, hindering accurate decoding of imagined movements …
Ocular Artifact Removal from EEG Data Using FCIF and FCFBCSP Algorithm with Modified DNN
S Akutthota, K Rajkumar, R Janapati - Congress on Control, Robotics, and …, 2024 - Springer
This research offers two innovative techniques to improve electroencephalography (EEG)-
based brain-computer interfaces (BCIs). The first technique, called Four-Class Iterative …
based brain-computer interfaces (BCIs). The first technique, called Four-Class Iterative …