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Neurofeedback

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From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system.

Dynamic feature selection applied to the recognition of grasping movements in the control of bioprosthetic hand.

Studies in health technology and informatics
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...

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
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...

Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.

Computational intelligence and neuroscience
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
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...

The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

Medical & biological engineering & computing
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...

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Human brain mapping
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...

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
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...

Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts.

NeuroImage
Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem...

Design and Implementation of a Novel Subject-Specific Neurofeedback Evaluation and Treatment System.

Annals of biomedical engineering
Electroencephalography (EEG)-based neurofeedback (NF) is a safe, non-invasive, non-painful method for treating various conditions. Current NF systems enable the selection of only one NF parameter, so that two parameters cannot be feedback simultaneou...