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A computational approach to developing cost-efficient adaptive-threshold algorithms for EEG neuro feedback
In electroencephalography (EEG) neurofeedback protocols, trainees receive feedback about the spectral power of the target brain wave oscillation and are tasked to increase or decrease this feedback signal compared to a predetermined threshold. In a recent computational analysis of a neurofeedback protocol it was shown that the placement of the threshold has a major impact on the learning rate and that placed too low or too high leads to no learning or even unlearning, respectively.
symbiosisonlinepublishing.com/quantitative-computational-...
A computational approach to developing cost-efficient adaptive-threshold algorithms for EEG neuro feedback
In electroencephalography (EEG) neurofeedback protocols, trainees receive feedback about the spectral power of the target brain wave oscillation and are tasked to increase or decrease this feedback signal compared to a predetermined threshold. In a recent computational analysis of a neurofeedback protocol it was shown that the placement of the threshold has a major impact on the learning rate and that placed too low or too high leads to no learning or even unlearning, respectively.
symbiosisonlinepublishing.com/quantitative-computational-...