AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Brain-Computer Interfaces

Showing 271 to 280 of 619 articles

Clear Filters

Neural Encoding and Decoding With Distributed Sentence Representations.

IEEE transactions on neural networks and learning systems
Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, ha...

Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification.

IEEE transactions on neural networks and learning systems
In the context of motor imagery, electroencephalography (EEG) data vary from subject to subject such that the performance of a classifier trained on data of multiple subjects from a specific domain typically degrades when applied to a different subje...

Adaptive Neural Control for a Class of Nonlinear Multiagent Systems.

IEEE transactions on neural networks and learning systems
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...

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

Journal of neuroengineering and rehabilitation
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...

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

BMC bioinformatics
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...

Building an adaptive interface via unsupervised tracking of latent manifolds.

Neural networks : the official journal of the International Neural Network Society
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...

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

Neural plasticity
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...

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

Neural networks : the official journal of the International Neural Network Society
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...

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

Neural plasticity
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...

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

Sensors (Basel, Switzerland)
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...