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-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects
Photovoltaic (PV) systems are subject to failures during their operation due to the aging
effects and external/environmental conditions. These faults may affect the different system …
effects and external/environmental conditions. These faults may affect the different system …
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 …
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 …
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 …
A fractional gradient descent algorithm robust to the initial weights of multilayer perceptron
X Xie, YF Pu, J Wang - Neural Networks, 2023 - Elsevier
For multilayer perceptron (MLP), the initial weights will significantly influence its
performance. Based on the enhanced fractional derivative extend from convex optimization …
performance. Based on the enhanced fractional derivative extend from convex optimization …
Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users
N Tibrewal, N Leeuwis, M Alimardani - Plos one, 2022 - journals.plos.org
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain
activity patterns associated with mental imagination of movement and convert them into …
activity patterns associated with mental imagination of movement and convert them into …
MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
P Autthasan, R Chaisaen… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …
control of several applications by decoding neurophysiological phenomena, which are …
Brain–computer interface-controlled exoskeletons in clinical neurorehabilitation: ready or not?
A Colucci, M Vermehren, A Cavallo… - … and neural repair, 2022 - journals.sagepub.com
The development of brain–computer interface-controlled exoskeletons promises new
treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting …
treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting …
A transformer-based deep neural network model for SSVEP classification
Steady-state visual evoked potential (SSVEP) is one of the most commonly used control
signals in the brain–computer interface (BCI) systems. However, the conventional spatial …
signals in the brain–computer interface (BCI) systems. However, the conventional spatial …