Prediction of brain-computer interface aptitude from individual brain structure.
about
Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework.Prediction of P300 BCI aptitude in severe motor impairment.Resting state functional connectivity predicts neurofeedback response.Transcranial magnetic stimulation for individual identification of the best electrode position for a motor imagery-based brain-computer interface.Quantifying the role of motor imagery in brain-machine interfacesThe morphology of midcingulate cortex predicts frontal-midline theta neurofeedback success.Learning to modulate one's own brain activity: the effect of spontaneous mental strategiesAre treatment effects of neurofeedback training in children with ADHD related to the successful regulation of brain activity? A review on the learning of regulation of brain activity and a contribution to the discussion on specificityTranscallosal Inhibition during Motor Imagery: Analysis of a Neural Mass Model.Predicting Inter-session Performance of SMR-Based Brain-Computer Interface Using the Spectral Entropy of Resting-State EEG.Closed-loop brain training: the science of neurofeedback.EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial.Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity.Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment.Ability to Gain Control Over One's Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study.Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design.EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches.Real-time decoding of covert attention in higher-order visual areas.White Matter Connectivity Pattern Associate with Characteristics of Scalp EEG Signals.Effect of somatosensory and neurofeedback training on balance in older healthy adults: a preliminary investigation.User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations.Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients.
P2860
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P2860
Prediction of brain-computer interface aptitude from individual brain structure.
description
2013 nî lūn-bûn
@nan
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
2013年论文
@zh
2013年论文
@zh-cn
name
Prediction of brain-computer interface aptitude from individual brain structure.
@ast
Prediction of brain-computer interface aptitude from individual brain structure.
@en
type
label
Prediction of brain-computer interface aptitude from individual brain structure.
@ast
Prediction of brain-computer interface aptitude from individual brain structure.
@en
prefLabel
Prediction of brain-computer interface aptitude from individual brain structure.
@ast
Prediction of brain-computer interface aptitude from individual brain structure.
@en
P2093
P2860
P50
P356
P1476
Prediction of brain-computer interface aptitude from individual brain structure
@en
P2093
P2860
P356
10.3389/FNHUM.2013.00105
P577
2013-04-02T00:00:00Z