Advances in Hybrid Brain‐Computer Interfaces: Principles, Design, and Applications
Z Li, S Zhang, J Pan - Computational Intelligence and …, 2019 - Wiley Online Library
Conventional brain‐computer interface (BCI) systems have been facing two fundamental
challenges: the lack of high detection performance and the control command problem. To …
challenges: the lack of high detection performance and the control command problem. To …
Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG
M Moghaddari, MZ Lighvan, S Danishvar - Computer Methods and …, 2020 - Elsevier
Abstract Background and objective Attention-Deficit/Hyperactivity Disorder (ADHD) is a
chronic behavioral disorder in children. Children with ADHD face many difficulties in …
chronic behavioral disorder in children. Children with ADHD face many difficulties in …
A hybrid method based on extreme learning machine and wavelet transform denoising for stock prediction
The trend prediction of the stock is a main challenge. Accidental factors often lead to short-
term sharp fluctuations in stock markets, deviating from the original normal trend. The short …
term sharp fluctuations in stock markets, deviating from the original normal trend. The short …
Motor imagery classification based on a recurrent-convolutional architecture to control a hexapod robot
T Mwata-Velu, J Ruiz-Pinales, H Rostro-Gonzalez… - Mathematics, 2021 - mdpi.com
Advances in the field of Brain-Computer Interfaces (BCIs) aim, among other applications, to
improve the movement capacities of people suffering from the loss of motor skills. The main …
improve the movement capacities of people suffering from the loss of motor skills. The main …
A deep neural network with subdomain adaptation for motor imagery brain-computer interface
M Zheng, B Yang - Medical Engineering & Physics, 2021 - Elsevier
Background The nonstationarity problem of EEG is very serious, especially for spontaneous
signals, which leads to the poor effect of machine learning related to spontaneous signals …
signals, which leads to the poor effect of machine learning related to spontaneous signals …
Acoustic emission-based flow noise detection and mechanism analysis for gas-liquid two-phase flow
N Zhao, C Li, H Jia, F Wang, Z Zhao, L Fang, X Li - Measurement, 2021 - Elsevier
The interphase forces of gas-liquid two-phase flow produce flow noise, which contains
abundant two-phase flow information, such as two-phase flow rate, flow pattern, void …
abundant two-phase flow information, such as two-phase flow rate, flow pattern, void …
Review of EEG feature selection by neural networks
I Rakhmatulin - International Journal of Science and Business, 2020 - papers.ssrn.com
The basis of the work of electroencephalography (EEG) is the registration of electrical
impulses from the brain using a special sensor or electrode. This method is used to treat and …
impulses from the brain using a special sensor or electrode. This method is used to treat and …
An introduction to BCI and its use in video games: a review
N Thomasson - Journal of Student Research, 2023 - jsr.org
The input of a video game can vary from a keyboard, a mouse, a controller, and a plethora of
other methods. The electroencephalogram (EEG) is a cap worn on the head that can detect …
other methods. The electroencephalogram (EEG) is a cap worn on the head that can detect …
Deep learning and machine learning for EEG signal processing on the example of recognizing the disease of alcoholism
R Ildar - medRxiv, 2021 - medrxiv.org
Alcoholism is one of the most common diseases in the world. This type of substance abuse
leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is …
leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is …
Motor imagery-based brain-computer interface: neural network approach
DM Lazurenko, VN Kiroy, IE Shepelev… - Optical Memory and …, 2019 - Springer
A neural network approach has been developed for detecting EEG patterns accompanying
the implementation of motor imagery, which are mental equivalents of real movements. The …
the implementation of motor imagery, which are mental equivalents of real movements. The …