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 …
Review of machine learning techniques for EEG based brain computer interface
S Aggarwal, N Chugh - Archives of Computational Methods in …, 2022 - Springer
A brain computer interface (BCI) framework uses computer algorithms to detect mental
activity patterns and manipulate external devices. Because of its simplicity and non …
activity patterns and manipulate external devices. Because of its simplicity and non …
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 …
Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification
The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …
correlation in the long-sequence data and visualizing the model. As time-series data, the …
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 …
A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals
Abstract In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is
converted into a control signal through processing a specific pattern in brain signals …
converted into a control signal through processing a specific pattern in brain signals …
Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …
through the utilization of brain waves. It is worth noting that the application of BCI is not …
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 …
A review of critical challenges in MI-BCI: From conventional to deep learning methods
Brain-computer interfaces (BCIs) have achieved significant success in controlling external
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …