[HTML][HTML] Hybrid brain–computer interface techniques for improved classification accuracy and increased number of commands: a review
In this paper, hybrid brain-computer interface (hBCI) technologies for improving
classification accuracy and increasing the number of commands are reviewed. Hybridization …
classification accuracy and increasing the number of commands are reviewed. Hybridization …
Brain-computer interface speller system for alternative communication: a review
Brain-computer interface (BCI) speller is a system that provides an alternative
communication for the disable people. The brain wave is translated into machine command …
communication for the disable people. The brain wave is translated into machine command …
A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals
An important subfield of brain–computer interface is the classification of motor imagery (MI)
signals where a presumed action, for example, imagining the hands' motions, is mentally …
signals where a presumed action, for example, imagining the hands' motions, is mentally …
Filtering techniques for channel selection in motor imagery EEG applications: a survey
Brain computer interface (BCI) systems are used in a wide range of applications such as
communication, neuro-prosthetic and environmental control for disabled persons using …
communication, neuro-prosthetic and environmental control for disabled persons using …
A logistic binary Jaya optimization-based channel selection scheme for motor-imagery classification in brain-computer interface
A Tiwari - Expert Systems with Applications, 2023 - Elsevier
BCI systems use motor imagery to allow users to control external devices through their brain
activity. They extract neural signals from the brain using a large number of EEG channels …
activity. They extract neural signals from the brain using a large number of EEG channels …
Improved SFFS method for channel selection in motor imagery based BCI
Background Multichannels used in brain–computer interface (BCI) systems contain
redundant information and cause inconvenience for practical application. Channel selection …
redundant information and cause inconvenience for practical application. Channel selection …
Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off
Electroencephalography (EEG) datasets are often small and high dimensional, owing to
cumbersome recording processes. In these conditions, powerful machine learning …
cumbersome recording processes. In these conditions, powerful machine learning …
Hand medical monitoring system based on machine learning and optimal EMG feature set
M Yu, G Li, D Jiang, G Jiang, B Tao, D Chen - Personal and Ubiquitous …, 2023 - Springer
Considering that serious hand function damage will greatly affect the daily life of patients, its
recovery mainly depends on the regular inspection and manual training of medical staff, and …
recovery mainly depends on the regular inspection and manual training of medical staff, and …
Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm
A Tiwari, A Chaturvedi - Multimedia Tools and Applications, 2023 - Springer
Abstract Multichannel Electroencephalography-based Brain-Computer Interface (BCI)
systems facilitate a communicating medium between the human brain and the outside world …
systems facilitate a communicating medium between the human brain and the outside world …
Automatic channel selection using multiobjective X-shaped binary butterfly algorithm for motor imagery classification
A Tiwari, A Chaturvedi - Expert Systems with Applications, 2022 - Elsevier
Multichannel EEG data processing is usually required to decode Motor Imagery (MI) specific
cognitive patterns in Brain-Computer Interface (BCI) systems. The signals from its channels …
cognitive patterns in Brain-Computer Interface (BCI) systems. The signals from its channels …