AIMC Topic: Neurofeedback
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A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 1, 2025
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Fun...
Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.
Current medical science
Nov 20, 2024
OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EE...
Bayesian learning from multi-way EEG feedback for robot navigation and target identification.
Scientific reports
Oct 7, 2023
Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously...
Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.
NeuroImage
May 25, 2021
Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of ...
The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments.
Scientific data
Feb 23, 2021
Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had th...
Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback.
Sensors (Basel, Switzerland)
Mar 14, 2020
Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability f...
A simulation-based approach to improve decoded neurofeedback performance.
NeuroImage
Apr 4, 2019
The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain acti...
Design and Implementation of a Novel Subject-Specific Neurofeedback Evaluation and Treatment System.
Annals of biomedical engineering
Feb 15, 2019
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...
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
Dec 16, 2018
OBJECTIVE: To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system.
Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts.
NeuroImage
Oct 11, 2018
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...
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