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 …
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 …
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
Electroencephalography (EEG) motor imagery (MI) signals have recently gained a lot of
attention as these signals encode a person's intent of performing an action. Researchers …
attention as these signals encode a person's intent of performing an action. Researchers …
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
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 …
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg… - Human brain …, 2017 - Wiley Online Library
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
Objective. Brain–computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally chosen from a …
using neural activity as the control signal. This neural signal is generally chosen from a …
EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their …
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …
with the environment. Recent advancements in technology and machine learning algorithms …
Learning temporal information for brain-computer interface using convolutional neural networks
Deep learning (DL) methods and architectures have been the state-of-the-art classification
algorithms for computer vision and natural language processing problems. However, the …
algorithms for computer vision and natural language processing problems. However, the …
EEG-based spatio–temporal convolutional neural network for driver fatigue evaluation
Driver fatigue evaluation is of great importance for traffic safety and many intricate factors
would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of …
would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of …