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Review of machine learning techniques for EEG based brain computer interface
S Aggarwal, N Chugh - Archives of Computational Methods in …, 2022 - Springer
A brain computer interface (BCI) framework uses computer algorithms to detect mental
activity patterns and manipulate external devices. Because of its simplicity and non …
activity patterns and manipulate external devices. Because of its simplicity and non …
A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface
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 …
communication through the utilization of neural activity generated due to kinesthetic …
Internal feature selection method of CSP based on L1-norm and Dempster–Shafer theory
The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for
feature extraction in motor imagery (MI)-based brain–computer interfaces (BCIs). However …
feature extraction in motor imagery (MI)-based brain–computer interfaces (BCIs). However …
Temporally constrained sparse group spatial patterns for motor imagery BCI
Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework
Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
[HTML][HTML] An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications
Recently, the rapid development of Artificial Intelligence (AI) applied in the Medical Internet
of Things (MIoT) for the diagnosis of diseases such as epilepsy based on the investigation of …
of Things (MIoT) for the diagnosis of diseases such as epilepsy based on the investigation of …
Learning common time-frequency-spatial patterns for motor imagery classification
The common spatial patterns (CSP) algorithm is the most popular spatial filtering method
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain …
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain …
A novel deep learning approach with data augmentation to classify motor imagery signals
Brain-computer interface provides a new communication bridge between the human mind
and devices, depending largely on the accurate classification and identification of non …
and devices, depending largely on the accurate classification and identification of non …
A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems
Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic
components or modes from electroencephalogram (EEG) signals for the development of …
components or modes from electroencephalogram (EEG) signals for the development of …
MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …
control of several applications by decoding neurophysiological phenomena, which are …