A review of the role of machine learning techniques towards brain–computer interface applications
S Rasheed - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
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 …
kNN and SVM classification for EEG: a review
This paper review the classification method of EEG signal based on k-nearest neighbor
(kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input …
(kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input …
EEG classification using sparse Bayesian extreme learning machine for brain–computer interface
Mu rhythm is a spontaneous neural response occurring during a motor imagery (MI) task
and has been increasingly applied to the design of brain–computer interface (BCI). Accurate …
and has been increasingly applied to the design of brain–computer interface (BCI). Accurate …
A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications
Abstract Background and Objective: Motor Imagery (MI) based Brain-Computer-Interface
(BCI) is a rising support system that can assist disabled people to communicate with the real …
(BCI) is a rising support system that can assist disabled people to communicate with the real …
[HTML][HTML] Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface
R Dhiman - Measurement: Sensors, 2023 - Elsevier
Abstract Decryption of Motor Imagery (MI) activity from an Electroencephalogram (EEG) data
is a significant part of the Brain-Computer Interface (BCI) technology that allows motor …
is a significant part of the Brain-Computer Interface (BCI) technology that allows motor …
Detection of motor imagery EEG signals employing Naïve Bayes based learning process
The objective of this study is to develop a reliable and robust analysis system that can
automatically detect motor imagery (MI) based electroencephalogram (EEG) signals for the …
automatically detect motor imagery (MI) based electroencephalogram (EEG) signals for the …
Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform
Motor imagery (MI) tasks-based brain–computer interface (BCI) system finds applications for
disabled people to communicate with surrounding. The BCI system reliability is relied on …
disabled people to communicate with surrounding. The BCI system reliability is relied on …
Spiking neural networks applied to the classification of motor tasks in EEG signals
JM Antelis, LE Falcón - Neural networks, 2020 - Elsevier
Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern
recognition, we report on the development and evaluation of brain signal classifiers based …
recognition, we report on the development and evaluation of brain signal classifiers based …
PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task
S Udhaya Kumar, H Hannah Inbarani - Neural Computing and …, 2017 - Springer
In recent years, most of the researchers are developing brain–computer interface (BCI)
applications for the physically disabled to be able to interconnect with peripheral devices …
applications for the physically disabled to be able to interconnect with peripheral devices …
Ensemble classifier for epileptic seizure detection for imperfect EEG data
Brain status information is captured by physiological electroencephalogram (EEG) signals,
which are extensively used to study different brain activities. This study investigates the use …
which are extensively used to study different brain activities. This study investigates the use …