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Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Deep learning-based electroencephalography analysis: a systematic review
Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …
of training, as well as advanced signal processing and feature extraction methodologies to …
Classification of hand movements from EEG using a deep attention-based LSTM network
Classifying limb movements using brain activity is an important task in Brain-computer
Interfaces (BCI) that has been successfully used in multiple application domains, ranging …
Interfaces (BCI) that has been successfully used in multiple application domains, ranging …
A LightGBM‐based EEG analysis method for driver mental states classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals
and families. Recently, electroencephalography‐(EEG‐) based physiological and brain …
and families. Recently, electroencephalography‐(EEG‐) based physiological and brain …
Toward a framework for trust building between humans and robots in the construction industry: A systematic review of current research and future directions
With the construction sector primed to incorporate such advanced technologies as artificial
intelligence (AI), robots, and machines, these advanced tools will require a deep …
intelligence (AI), robots, and machines, these advanced tools will require a deep …
A review of the role of machine learning techniques towards brain–computer interface applications
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
EEG based brain computer interface for controlling a robot arm movement through thought
R Bousseta, I El Ouakouak, M Gharbi, F Regragui - Irbm, 2018 - Elsevier
Abstract Background The Brain Computer Interfaces (BCI) are devices allowing direct
communication between the brain of a user and a machine. This technology can be used by …
communication between the brain of a user and a machine. This technology can be used by …
Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search …
This paper presents an automatic method for finding optimal channels in Brain Computer
Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in …
Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in …
[HTML][HTML] Evaluation of machine learning algorithms for classification of EEG signals
In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …
[HTML][HTML] A survey on robots controlled by motor imagery brain-computer interfaces
J Zhang, M Wang - Cognitive Robotics, 2021 - Elsevier
A brain-computer interface (BCI) can provide a communication approach conveying brain
information to the outside. Especially, the BCIs based on motor imagery play the important …
information to the outside. Especially, the BCIs based on motor imagery play the important …