A comprehensive review of endogenous EEG-based BCIs for dynamic device control

N Padfield, K Camilleri, T Camilleri, S Fabri, M Bugeja - Sensors, 2022 - mdpi.com
Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel
approach for controlling external devices. BCI technologies can be important enabling …

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 …

Temporal–spatial transformer based motor imagery classification for BCI using independent component analysis

A Hameed, R Fourati, B Ammar, A Ksibi… - … Signal Processing and …, 2024 - Elsevier
Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …

Lightweight privacy-preserving feature extraction for EEG signals under edge computing

N Yan, H Cheng, X Liu, F Chen… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
The health-related Internet of Things (IoT) plays an irreplaceable role in the collection,
analysis, and transmission of medical data. As a device of the health-related IoT, the …

Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model

Y Wang, F Cui, X Ding, Y Yao, G Li, G Gui… - … Signal Processing and …, 2023 - Elsevier
Objective Coal workers' pneumoconiosis (CWP) is a broad and serious occupational
disease caused by inhaling coal dust, which can cause permanent physical injury. There is …

Flexible coding scheme for robotic arm control driven by motor imagery decoding

Q Ai, M Zhao, K Chen, X Zhao, L Ma… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Brain computer interface (BCI) technology is an innovative way of information
exchange, which can effectively convert physiological signals into control instructions of …

Subject-independent EEG classification of motor imagery based on dual-branch feature fusion

Y Dong, X Wen, F Gao, C Gao, R Cao, J Xiang, R Cao - Brain Sciences, 2023 - mdpi.com
A brain computer interface (BCI) system helps people with motor dysfunction interact with
the external environment. With the advancement of technology, BCI systems have been …

An effective EEG signal-based sleep staging system using machine learning techniques

SK Satapathy, S Thakkar, A Patel… - 2022 IEEE 6th …, 2022 - ieeexplore.ieee.org
Single-channel electroencephalography (EEG) is the most popular choice of sensing
modality in sleep staging studies because it widely conforms to sleep staging guidelines …

Classification of motor imagery EEG with ensemble RNCA model

T Thenmozhi, R Helen, S Mythili - Behavioural Brain Research, 2025 - Elsevier
Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the
motor functions of neurophysiologically affected persons. But the performance of MI tasks is …

Pre-movement pattern decoding from motor evoked potentials for reducing interaction delay

R Fu, F Xu, H Liang, Y Liu, S Wang, Y Wang… - … Signal Processing and …, 2025 - Elsevier
Brain-computer interfaces (BCIs) represent one of the most successful integrations of
neuroscience and artificial intelligence, and have been applied in intelligent healthcare …