EEG-based BCIs on motor imagery paradigm using wearable technologies: a systematic review
In recent decades, the automatic recognition and interpretation of brain waves acquired by
electroencephalographic (EEG) technologies have undergone remarkable growth, leading …
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 …
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
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 …
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 …
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
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 …
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
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 …
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
Sažetak The activities for motor imagery (MI) movements in Electroencephalography (EEG)
are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals …
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 …
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 …
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 …
imagery. sparse representation classification (SRC) appears as an alternative for …