AIMC Topic: Neurofeedback
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Energy-Optimal Human Walking With Feedback-Controlled Robotic Prostheses: A Computational Study.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 20, 2018
Lower-limb amputees typically experience reduced mobility and higher metabolic rates than non-amputees. It may be possible to improve their mobility and metabolic rate with an optimized robotic prosthesis. Here, we use large-scale trajectory optimiza...
Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.
Human brain mapping
Jan 16, 2018
Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI pe...
The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.
Medical & biological engineering & computing
Jul 8, 2017
Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented ta...
Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.
Computational intelligence and neuroscience
Jul 26, 2016
Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be...
Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation.
PloS one
Feb 16, 2016
In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activit...
Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?
Journal of neural engineering
Jul 14, 2015
OBJECTIVE: Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these inter...
EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.
Human brain mapping
Jun 15, 2024
Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG usi...
Neurorobotics for neurorehabilitation.
Science (New York, N.Y.)
Aug 6, 2021
A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold.
International journal of neural systems
Mar 1, 2020
Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-va...
Dynamic feature selection applied to the recognition of grasping movements in the control of bioprosthetic hand.
Studies in health technology and informatics
Jan 1, 2015
The paper presents novel method of dynamic feature selection (DFS) and its application in the problem of recognition of patient intent in the bioprosthesis control system. In the proposed approach features are selected dynamically, i.e. separately fo...
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