Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model
Motor Imagery classification is a major topic in Brain-Computer Interface (BCI) because of its
value for clinical restoration of impaired motor ability. Compared to the classical approaches …
value for clinical restoration of impaired motor ability. Compared to the classical approaches …
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 …
Global research on artificial intelligence-enhanced human electroencephalogram analysis
The application of artificial intelligence (AI) technologies in assisting human
electroencephalogram (EEG) analysis has become an active scientific field. This study aims …
electroencephalogram (EEG) analysis has become an active scientific field. This study aims …
A hybrid-domain deep learning-based BCI for discriminating hand motion planning from EEG sources
In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to
decode hand movement preparation phases from electroencephalographic (EEG) …
decode hand movement preparation phases from electroencephalographic (EEG) …
Continuous EEG decoding of pilots' mental states using multiple feature block-based convolutional neural network
Non-invasive brain-computer interface (BCI) has been developed for recognizing and
classifying human mental states with high performances. Specifically, classifying pilots' …
classifying human mental states with high performances. Specifically, classifying pilots' …
Motor imagery classification using inter-task transfer learning via a channel-wise variational autoencoder-based convolutional neural network
Highly sophisticated control based on a brain-computer interface (BCI) requires decoding
kinematic information from brain signals. The forearm is a region of the upper limb that is …
kinematic information from brain signals. The forearm is a region of the upper limb that is …
[HTML][HTML] Eeg-based mental tasks recognition via a deep learning-driven anomaly detector
This paper introduces an unsupervised deep learning-driven scheme for mental tasks'
recognition using EEG signals. To this end, the Multichannel Wiener filter was first applied to …
recognition using EEG signals. To this end, the Multichannel Wiener filter was first applied to …
Rethinking CNN architecture for enhancing decoding performance of motor imagery-based EEG signals
Brain–computer interface (BCI) is a technology that allows users to control computers by
reflecting their intentions. Electroencephalogram (EEG)–based BCI has been developed …
reflecting their intentions. Electroencephalogram (EEG)–based BCI has been developed …
[HTML][HTML] Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network
R Zhang, Y Chen, Z Xu, L Zhang, Y Hu… - Frontiers in …, 2023 - frontiersin.org
Motor imagery-based brain-computer interfaces (MI-BCI) have important application values
in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral …
in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral …