Eyes-Closed Resting EEG Predicts the Learning of Alpha Down-Regulation in Neurofeedback Training

• Published in: Frontiers in psychology 2018-08, Vol.9, p.1607-1607
• Main Authors: Nan, Wenya, Wan, Feng, Tang, Qi, Wong, Chi Man, Wang, Boyu, Rosa, Agostinho
• Format: Article
• Language: English
• Subjects: alpha; down-regulation; learning; neurofeedback; Psychology; resting baseline
• ISSN: 1664-1078; 1664-1078
• DOI: 10.3389/fpsyg.2018.01607

Neurofeedback training, which enables the trainee to learn self-control of the EEG activity of interest based on online feedback, has demonstrated benefits on cognitive and behavioral performance. Nevertheless, as a core mechanism of neurofeedback, learning of EEG regulation (i.e., EEG learning) has not been well understood. Moreover, a substantial number of non-learners who fail to achieve successful EEG learning have often been reported. This study investigated the EEG learning in alpha down-regulation neurofeedback, aiming to better understand the alpha learning and to early predict learner/non-learner. Twenty-nine participants received neurofeedback training to down-regulate alpha in two days, while eight of them were identified as non-learners who failed to reduce their alpha within sessions. Through a stepwise linear discriminant analysis, a prediction model was built based on participant's eyes-closed resting EEG activities in broad frequency bands including lower alpha, theta, sigma and beta 1 measured before training, which was validated in predicting learners/non-learners. The findings would assist in the early identification of the individuals who would not likely reduce their alpha during neurofeedback.