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 review on transfer learning in EEG signal analysis
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
Deep learning for motor imagery EEG-based classification: A review
Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …
rapidly advances and inventions in deep learning techniques, and highly powerful and …
Brain-computer interface: Advancement and challenges
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
BENDR: Using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are
commonly expected to learn general features when trained across a variety of contexts, such …
commonly expected to learn general features when trained across a variety of contexts, such …
EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their …
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …
with the environment. Recent advancements in technology and machine learning algorithms …
An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces
AM Roy - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
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 …
A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification
J Wang, S Cheng, J Tian, Y Gao - Biomedical Signal Processing and …, 2023 - Elsevier
Motor imagery-based brain–computer interaction (MI-BCI) converts human neural activity
into computational information, often used as commands, by recognizing …
into computational information, often used as commands, by recognizing …
Vgg16 feature extractor with extreme gradient boost classifier for pancreas cancer prediction
The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly
improved by an early and accurate diagnosis. Several studies have created automated …
improved by an early and accurate diagnosis. Several studies have created automated …