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 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 …
Data augmentation for deep-learning-based electroencephalography
Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …
considerable performance gains for deep learning (DL)—increased accuracy and stability …
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 …
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface
F Mattioli, C Porcaro… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Brain-computer interface (BCI) aims to establish communication paths between
the brain processes and external devices. Different methods have been used to extract …
the brain processes and external devices. Different methods have been used to extract …
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 …
Consumer grade EEG measuring sensors as research tools: A review
P Sawangjai, S Hompoonsup… - IEEE Sensors …, 2019 - ieeexplore.ieee.org
Since the launch of the first consumer grade EEG measuring sensorsNeuroSky Mindset'in
2007, the market has witnessed an introduction of at least one new product every year by …
2007, the market has witnessed an introduction of at least one new product every year by …
Complex networks and deep learning for EEG signal analysis
Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …
representation of the human's physiological and pathological states. Up to now, much work …
EEG signal classification using LSTM and improved neural network algorithms
Neural network (NN) finds role in variety of applications due to combined effect of feature
extraction and classification availability in deep learning algorithms. In this paper, we have …
extraction and classification availability in deep learning algorithms. In this paper, we have …
Convolutional neural network for drowsiness detection using EEG signals
S Chaabene, B Bouaziz, A Boudaya, A Hökelmann… - Sensors, 2021 - mdpi.com
Drowsiness detection (DD) has become a relevant area of active research in biomedical
signal processing. Recently, various deep learning (DL) researches based on the EEG …
signal processing. Recently, various deep learning (DL) researches based on the EEG …