Progressive Thresholding: Shaping and Specificity in Automated Neurofeedback Training
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Neurofeedback has long been proposed as a promising form of adjunctive non-pharmaceutical treatment for a variety of neuropsychological disorders. However, there is much debate over its efficacy and specificity. Many suggest that specificity can only be achieved when a specially trained clinician manually updates reward thresholds that indicate to the trainee when they are modulating their brain activity correctly, during training. We present a novel fully automated reward thresholding algorithm called progressive thresholding and test it with a frontal alpha asymmetry neurofeedback protocol. Progressive thresholding uses dynamic difficulty tuning and individual-specific progress models to simulate the shaping a clinician might perform when setting reward thresholds manually. We demonstrate in a double-blind comparison that progressive thresholding leads to significantly better learning outcomes compared with current automatic reward thresholding algorithms.
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