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http://dx.doi.org/10.15701/kcgs.2019.25.2.19

Auto Thresholding for Efficient Neurofeedback Trainning  

Shin, Min-Chul (Suinjae Brain Science)
Hwang, Hae-Do (Department of Multimedia Engineering, Dongguk University)
Yoon, Seung-Hyun (Department of Multimedia Engineering, Dongguk University)
Lee, Jieun (Division of IT Convergence Engineering, Hansung University)
Publication Information
Journal of the Korea Computer Graphics Society / v.25, no.2, 2019 , pp. 19-29 More about this Journal
Abstract
We develop a complete system that includes data collection, signal processing, and real-time interaction for effective neurofeedback training. Our system supports a sophisticated technique to find threshold values which are quite important for effective neurofeedback system. A therapist specifies a target success rate of positive feedback, allowable error and time. The system computes a current success rate and compare it with the target one. If the difference between two rates exceeds the allowable error for allowable time, we find an optimum threshold value to obtain the target success rate by using numerical optimization technique. We conduct several experiments by varying input parameters: target success rate, allowable error and time, and demonstrate the effectiveness of our technique by showing the desired target success rate is stably obtained and systematically controlled by input parameters.
Keywords
Neurofeedback; Serious Game; Auto Thresholding; EEG Trainning;
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