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Real-Time EEG Neurofeedback Using a Subject-Independent Classifier in Women With Depressive Symptoms

Pontificia Universidad Catolica de Chile · 2019
ClinicalTrials.gov · Datasets · License: Public Domain · 2019
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brain-computer-interfacenaneurofeedbackneuromodulationsupport-vector-machine
This completed feasibility study examined whether right-handed women aged 18-35 years with mild-to-moderate depressive symptoms could use real-time electroencephalography (EEG) neurofeedback based on a subject-independent classifier to regulate brain activity toward a classifier-defined reference pattern. Participants were randomly assigned to receive either classifier-contingent visual feedback with performance-contingent monetary rewards or noncontingent sham visual feedback with yoked monetary rewards. All participants completed five training sessions over approximately 3 weeks. The primary purpose was to determine whether overall classifier accuracy during training exceeded the theoretical 50% chance level. Changes in clinician-rated and self-reported depressive symptoms, associations between classifier performance and symptom change, EEG spatial patterns, and procedural tolerability were exploratory.
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