EEG-based BCIs on motor imagery paradigm using wearable technologies: a systematic review

A Saibene, M Caglioni, S Corchs, F Gasparini - Sensors, 2023 - mdpi.com
In recent decades, the automatic recognition and interpretation of brain waves acquired by
electroencephalographic (EEG) technologies have undergone remarkable growth, leading …

Systematic analysis of a military wearable device based on a multi-level fusion framework: research directions

H Shi, H Zhao, Y Liu, W Gao, SC Dou - Sensors, 2019 - mdpi.com
With the development of the Internet of Battlefield Things (IoBT), soldiers have become key
nodes of information collection and resource control on the battlefield. It has become a trend …

Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals

JH Jeong, KH Shim, DJ Kim… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to
control external devices. This paper presents the decoding of intuitive upper extremity …

Motor imagery EEG classification based on flexible analytic wavelet transform

Y You, W Chen, T Zhang - Biomedical Signal Processing and Control, 2020 - Elsevier
Motor imagery electroencephalogram (MI-EEG) based brain-computer interface (BCI) is a
burgeoning auxiliary means to realize rehabilitation therapy. One of the major concerns in …

Imaginary finger movements decoding using empirical mode decomposition and a stacked BiLSTM architecture

T Mwata-Velu, JG Avina-Cervantes, JM Cruz-Duarte… - Mathematics, 2021 - mdpi.com
Motor Imagery Electroencephalogram (MI-EEG) signals are widely used in Brain-Computer
Interfaces (BCI). MI-EEG signals of large limbs movements have been explored in recent …

Decoding multi-class EEG signals of hand movement using multivariate empirical mode decomposition and convolutional neural network

Y Tao, W Xu, G Wang, Z Yuan, M Wang… - … on Neural Systems …, 2022 - ieeexplore.ieee.org
Brain-computer interface (BCI) is a technology that connects the human brain and external
devices. Many studies have shown the possibility of using it to restore motor control in stroke …

Motor imagery EEG recognition using deep generative adversarial network with EMD for BCI applications

S Stephe, KV Kumar - Tehnički vjesnik, 2022 - hrcak.srce.hr
Sažetak The activities for motor imagery (MI) movements in Electroencephalography (EEG)
are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals …

Machine learning approach for the classification of EEG signals of multiple imagery tasks

S Tiwari, S Goel, A Bhardwaj - 2020 11th International …, 2020 - ieeexplore.ieee.org
Electroencephalogram (EEG) signals can be used to capture the electrical pattern
generated on the surface of the human brain. The electrical activity in terms of EEG signals …

Robotic orthosis compared to virtual hand for brain–computer interface feedback

J Cantillo-Negrete, RI Carino-Escobar… - Biocybernetics and …, 2019 - Elsevier
Abstract Brain–Computer Interfaces (BCI) allow the control of external devices by decoding
the users' intentions from their central nervous system. Feedback, one of the main elements …

Motor imagery classification using sparse representations: an exploratory study

JAA de Menezes, JC Gomes, V de Carvalho Hazin… - Scientific Reports, 2023 - nature.com
The non-stationary nature of the EEG signal poses challenges for the classification of motor
imagery. sparse representation classification (SRC) appears as an alternative for …