A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface

A Singh, AA Hussain, S Lal, HW Guesgen - Sensors, 2021 - mdpi.com
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …

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 …

Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications

G Varone, W Boulila, M Driss, S Kumari, MK Khan… - Information …, 2024 - Elsevier
Abstract A Brain–Computer Interface (BCI), integrated with the Internet of Medical Things
(IoMT) and based on electroencephalogram (EEG) technology, allows users to control …

A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning

F Li, F He, F Wang, D Zhang, Y Xia, X Li - Applied Sciences, 2020 - mdpi.com
Left and right hand motor imagery electroencephalogram (MI-EEG) signals are widely used
in brain-computer interface (BCI) systems to identify a participant intent in controlling …

A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions

S Khademi, M Neghabi, M Farahi, M Shirzadi… - … Intelligence-Based Brain …, 2022 - Elsevier
Brain-computer interface (BCI) aims to translate human intention into a control output signal.
In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity …

Design of an automatic hybrid system for removal of eye-blink artifacts from EEG recordings

S Çınar - Biomedical Signal Processing and Control, 2021 - Elsevier
Electroencephalography (EEG) signals are frequently used in several areas, such as
diagnosis of diseases and BCI applications. It is important to remove noise sources for …

To explore the potentials of independent component analysis in brain-computer interface of motor imagery

X Wu, B Zhou, Z Lv, C Zhang - IEEE Journal of Biomedical and …, 2019 - ieeexplore.ieee.org
This paper is focused on the experimental approach to explore the potential of independent
component analysis (ICA) in the context of motor imagery (MI)-based brain-computer …

A multi-view SVM approach for seizure detection from single channel EEG signals

GC Jana, MS Praneeth, A Agrawal - IETE Journal of Research, 2023 - Taylor & Francis
Seizures are the part of the epilepsy that occurs in central nervous system which leads to
abnormal brain activity. Electroencephalogram (EEG) signal recordings are mostly used in …

Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification

X Xie, L Chen, S Qin, F Zha, X Fan - Frontiers in Neurorobotics, 2024 - frontiersin.org
Introduction As an interactive method gaining popularity, brain-computer interfaces (BCIs)
aim to facilitate communication between the brain and external devices. Among the various …

Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface

S Wang, Z Luo, S Zhao, Q Zhang, G Liu, D Wu, E Yin… - Bioengineering, 2023 - mdpi.com
In brain-computer interface (BCI) systems, challenges are presented by the recognition of
motor imagery (MI) brain signals. Established recognition approaches have achieved …