Neurofeedback training for children with ADHD using individual beta rhythm

Abstract

Neurofeedback training (NFT) is a noninvasive neuromodulation method for children with attention-deficit/hyperactivity disorder (ADHD). Brain rhythms, the unique pattern in electroencephalogram (EEG), are widely used as the training target. Most of current studies used a fixed frequency division of brain rhythms, which ignores the individual developmental difference of each child. In this study, we validated the feasibility of NFT using individual beta rhythm. A total of 55 children with ADHD were divided into two groups using the relative power of individual or fixed beta rhythms as the training index. ADHD rating scale (ADHD-RS) was completed before and after NFT, and the EEG and behavioral features were extracted during the training process. After intervention, the attention ability of both groups was significantly improved, showing a significant increase in beta power, a decrease in scores of ADHD-RS and an improvement in behavioral and other EEG features. The training effect was significantly better with individualized beta training, showing more improvement in ADHD-RS scores. Furthermore, the distribution of brain rhythms moved towards high frequency after intervention. This study demonstrates the effectiveness of NFT based on individual beta rhythm for the intervention of children with ADHD. When designing a NFT protocol and the corresponding data analysis process, an individualized brain rhythm division should be applied to reflect the actual brain state and to accurately evaluate the effect of NFT.

Keywords: ADHD; Attention; EEG; Individual rhythm; Neurofeedback.

Fig. 1

The graphical interface for visual feedback during NFT

Fig. 2

ADHD-RS scores of the two groups before and after NFT. a Inattention scores; b hyperactivity scores; c total scores. The vertical line represents the standard deviation of the feature values

Fig. 3

Changes in behavioral features of the two groups with 20 training sessions. a Reward rate of the iBeta group; b reward rate of the Beta group; c maximum duration of the iBeta group; d maximum duration of the Beta group. The diamond in each subfigure represents the mean value of the feature values; the shaded area represents the standard deviation. Both groups showed significant training effect and no significant group effect on reward rate, but showed no training and group effect on maximum duration

Fig. 4

Change in iAPF of the two groups in the 20 training sessions. a iBeta group; b Beta group. The iAPF of both groups gradually increased during NFT

Fig. 5

The two-tailed Pearson correlation between iBeta difference and difference of ADHD-RS scores of iBeta group. a Correlation with a decrease in inattention score; b correlation with a decrease in hyperactivity score; c correlation with a decrease in total score

Fig. 6

The two-tailed Pearson correlation between iBeta differences and the differences in behavioral features of both groups. a Correlation with increase of reward rates; b correlation with increase of maximum duration

Fig. 7

The two-tailed Pearson correlation between iBeta differences and EEG features of both groups. a Correlation with the decrease in iTheta; b correlation with the increase in iGamma; c correlation with the increase of iAPF