AIMC Topic: Brain-Computer Interfaces

Clear Filters Showing 301 to 310 of 661 articles

Adaptive Neural Control for a Class of Nonlinear Multiagent Systems.

IEEE transactions on neural networks and learning systems Feb 4, 2021
This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODE...
Nonlinear Dynamics Brain-Computer Interfaces Uncertainty Algorithms Feedback Neural Networks, Computer
View on PubMed DOI

Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review.

Journal of neuroengineering and rehabilitation Jan 23, 2021
BACKGROUND: Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we repor...
Humans Female Brain-Computer Interfaces Robotics Motor Skills Male Hand Middle Aged Activities of Daily Living Stroke Rehabilitation
View on PubMed DOI

A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1).

BMC bioinformatics Jan 22, 2021
BACKGROUND: Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled lim...
Brain-Computer Interfaces Movement Motor Cortex Rats Least-Squares Analysis Neural Networks, Computer Animals
View on PubMed DOI

Building an adaptive interface via unsupervised tracking of latent manifolds.

Neural networks : the official journal of the International Neural Network Society Jan 20, 2021
In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, du...
Humans Unsupervised Machine Learning Brain-Computer Interfaces Computer Security Calibration
View on PubMed DOI

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.

Neural plasticity Jan 13, 2021
Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality fli...
Algorithms Computer Simulation Animals Humans Artificial Intelligence Flight, Animal Bees Brain-Computer Interfaces Aircraft
View on PubMed DOI

Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network.

Neural networks : the official journal of the International Neural Network Society Dec 23, 2020
In recent years, deep learning has emerged as a powerful tool for developing Brain-Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been mar...
Brain-Computer Interfaces Imagination Transfer, Psychology Hand Psychomotor Performance Female Young Adult Brain Male Neural Networks, Computer Electroencephalography Machine Learning Algorithms Humans Adult
View on PubMed DOI

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.

Neural plasticity Dec 7, 2020
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based...
Brain-Computer Interfaces Movement Imagination Humans Neural Networks, Computer Brain Deep Learning Databases, Factual
View on PubMed DOI

Reducing Response Time in Motor Imagery Using A Headband and Deep Learning.

Sensors (Basel, Switzerland) Nov 25, 2020
Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to mach...
Electroencephalography Reaction Time Brain-Computer Interfaces Humans Deep Learning Algorithms Wearable Electronic Devices
View on PubMed DOI

Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke.

IEEE transactions on bio-medical engineering Nov 19, 2020
OBJECTIVE: This randomized controlled feasibility study investigates the ability for clinical application of the Brain-Computer Interface-based Soft Robotic Glove (BCI-SRG) incorporating activities of daily living (ADL)-oriented tasks for stroke reha...
Electroencephalography Stroke Brain-Computer Interfaces Upper Extremity Activities of Daily Living Humans Treatment Outcome Robotics Stroke Rehabilitation
View on PubMed DOI

Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

Sensors (Basel, Switzerland) Nov 5, 2020
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the s...
Algorithms Calibration Brain Brain-Computer Interfaces Electroencephalography Humans Machine Learning
View on PubMed DOI
Popular Topics
Recent Journals