Brain-computer interface: Advancement and challenges
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
Decoding covert speech from EEG-a comprehensive review
JT Panachakel, AG Ramakrishnan - Frontiers in Neuroscience, 2021 - frontiersin.org
Over the past decade, many researchers have come up with different implementations of
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …
A deep transfer convolutional neural network framework for EEG signal classification
Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
An end-to-end deep learning approach to MI-EEG signal classification for BCIs
H Dose, JS Møller, HK Iversen… - Expert Systems with …, 2018 - Elsevier
Goal: To develop and implement a Deep Learning (DL) approach for an
electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) …
electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) …
A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry
A brain-computer interface (BCI) facilitates a medium to translate the human motion
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features
Objective: Recently, electroencephalography (EEG)-based brain-computer interfaces (BCIs)
have made tremendous progress in increasing communication speed. However, current BCI …
have made tremendous progress in increasing communication speed. However, current BCI …
Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network
X Tang, W Li, X Li, W Ma, X Dang - Expert Systems with Applications, 2020 - Elsevier
Electroencephalogram (EEG) signals classification plays a crucial role in brain computer
interfaces (BCIs) system. However, the inherent complex properties of EEG signals make it …
interfaces (BCIs) system. However, the inherent complex properties of EEG signals make it …
Cycle-by-cycle analysis of neural oscillations
Neural oscillations are widely studied using methods based on the Fourier transform, which
models data as sums of sinusoids. This has successfully uncovered numerous links …
models data as sums of sinusoids. This has successfully uncovered numerous links …
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 …
Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal
PT Krishnan, ANJ Raj, P Balasubramanian… - Biocybernetics and …, 2020 - Elsevier
Multivariate analysis of the EEG signal for the detection of Schizophrenia condition is
proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose …
proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose …