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 …
Deep multi-view learning methods: A review
Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …
success by exploiting complementary information of multiple features or modalities …
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 …
imagery (MI) has emerged as a powerful method for establishing direct communication …
Physics-informed attention temporal convolutional network for EEG-based motor imagery classification
H Altaheri, G Muhammad… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
The brain-computer interface (BCI) is a cutting-edge technology that has the potential to
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
Background: Analysis and classification of extensive medical data (eg
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
[HTML][HTML] Identification of motor and mental imagery EEG in two and multiclass subject-dependent tasks using successive decomposition index
The development of fast and robust brain–computer interface (BCI) systems requires non-
complex and efficient computational tools. The modern procedures adopted for this purpose …
complex and efficient computational tools. The modern procedures adopted for this purpose …
A survey on deep learning classification algorithms for motor imagery
B Guragai, O AlShorman, M Masadeh… - 2020 32nd …, 2020 - ieeexplore.ieee.org
In recent years, motor imagery electroencephalography decoding has become a promising
research field in brain-computer interface. Motor imagery signals generated from the brain …
research field in brain-computer interface. Motor imagery signals generated from the brain …
Dynamic convolution with multilevel attention for EEG-based motor imagery decoding
H Altaheri, G Muhammad… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Brain–computer interface (BCI) is an innovative technology that utilizes artificial intelligence
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
Comparative analysis of spectral and temporal combinations in CSP-based methods for decoding hand motor imagery tasks
Background A widely used paradigm for brain-computer interfaces (BCI) is based on the
detection of event-related (des) synchronization (ERD/S) in response to hand motor imagery …
detection of event-related (des) synchronization (ERD/S) in response to hand motor imagery …
A multi-scale fusion CNN model based on adaptive transfer learning for multi-class MI-classification in BCI system
AM Roy - BioRxiv, 2022 - biorxiv.org
Deep learning-based brain-computer interface (BCI) in motor imagery (MI) has emerged as
a powerful method for establishing direct communication between the brain and external …
a powerful method for establishing direct communication between the brain and external …