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 …
A comprehensive survey on multimodal medical signals fusion for smart healthcare systems
Smart healthcare is a framework that utilizes technologies such as wearable devices, the
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …
Physics-informed attention temporal convolutional network for EEG-based motor imagery classification
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 …
A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification
Electroencephalography-based motor imagery (EEG-MI) classification is a critical
component of the brain-computer interface (BCI), which enables people with physical …
component of the brain-computer interface (BCI), which enables people with physical …
MAtt: A manifold attention network for EEG decoding
YT Pan, JL Chou, CS Wei - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-
invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL) …
invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL) …
A compact multi-branch 1D convolutional neural network for EEG-based motor imagery classification
X Liu, S **ong, X Wang, T Liang, H Wang… - … Signal Processing and …, 2023 - Elsevier
Motor imagery (MI) EEG signals are considered a promising paradigm for BCI systems that
enable humans to communicate with the outside world through the brain and have a wide …
enable humans to communicate with the outside world through the brain and have a wide …
EEG-ITNet: An explainable inception temporal convolutional network for motor imagery classification
In recent years, neural networks and especially deep architectures have received
substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs) …
substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs) …
Attention-inception and long-short-term memory-based electroencephalography classification for motor imagery tasks in rehabilitation
In recent years, the contributions of deep learning have had a phenomenal impact on
electroencephalography-based brain-computer interfaces. While the decoding accuracy of …
electroencephalography-based brain-computer interfaces. While the decoding accuracy of …
Enhanced grasshopper optimization algorithm with extreme learning machines for motor‐imagery classification
KR Balmuri, SR Madala, PB Divakarachari… - Asian Journal of …, 2023 - Wiley Online Library
Abstract In Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …
Motor imagery electroencephalography channel selection based on deep learning: a shallow convolutional neural network
HK Amiri, M Zarei, MR Daliri - Engineering Applications of Artificial …, 2024 - Elsevier
Electroencephalography (EEG) motor imagery (MI) signals have recently attracted much
attention because of their potential to communicate with the surrounding environment in a …
attention because of their potential to communicate with the surrounding environment in a …