Classification of motor imagery EEG signals based on deep autoencoder and convolutional neural network approach
JF Hwaidi, TM Chen - IEEE access, 2022 - ieeexplore.ieee.org
The technology of the brain-computer interface (BCI) employs electroencephalogram (EEG)
signals to establish direct interaction between the human body and its surroundings with …
signals to establish direct interaction between the human body and its surroundings with …
Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI.
Abstract Brain–Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor
mental activity with the ultimate objective of allowing people to communicate with computers …
mental activity with the ultimate objective of allowing people to communicate with computers …
Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation
This paper proposes a novel feature selection method utilizing Rényi min-entropy-based
algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet …
algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet …
Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine
Cognitive load level identification is an interesting challenge in the field of brain-computer-
interface. The sole objective of this work is to classify different cognitive load levels from …
interface. The sole objective of this work is to classify different cognitive load levels from …
Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network
Practical brain–computer interface (BCI) demands the learning-based adaptive model that
can handle diverse problems. To implement a BCI, usually functional near-infrared …
can handle diverse problems. To implement a BCI, usually functional near-infrared …
The improved ELM algorithms optimized by bionic WOA for EEG classification of brain computer interface
Z Lian, L Duan, Y Qiao, J Chen, J Miao, M Li - IEEE Access, 2021 - ieeexplore.ieee.org
The breakthrough of electroencephalogram (EEG) signal classification of brain computer
interface (BCI) will set off another technological revolution of human computer interaction …
interface (BCI) will set off another technological revolution of human computer interaction …
Machine learning approach for the classification of EEG signals of multiple imagery tasks
Electroencephalogram (EEG) signals can be used to capture the electrical pattern
generated on the surface of the human brain. The electrical activity in terms of EEG signals …
generated on the surface of the human brain. The electrical activity in terms of EEG signals …
Common spatial pattern in frequency domain for feature extraction and classification of multichannel EEG signals
The extraction methodology of the significant features from the signals is one of the most
important pre-requisite steps for EEG signal classification. Common spatial pattern (CSP) is …
important pre-requisite steps for EEG signal classification. Common spatial pattern (CSP) is …
A Methodological Review on EEG Data Reduction in Edge/Fog computing-based IoMT networks
This paper presents a methodological review of electroencephalography (EEG) data
reduction in edge/fog computing-based IoMT networks. The review focuses on the existing …
reduction in edge/fog computing-based IoMT networks. The review focuses on the existing …
Enhanced EEG-based cognitive workload detection using RADWT and machine learning
Understanding cognitive workload improves learning performance and provides insights
into human cognitive processes. Estimating cognitive workload finds practical applications in …
into human cognitive processes. Estimating cognitive workload finds practical applications in …