A Portable Neurofeedback Training System for Attention Improvement Based on High-Performance Edge CNN Accelerator

Chunhua He

Abstract: Currently, many people around the world, especially children and youth, are facing the problem of attention deficit. neurofeedback training (NFT) is proved to be an effective method for improving the attention level. However, current NFT typically requires the use of computers or other nonportable devices, which limits the application scenarios of this technique. Therefore, a portable NFT system based on high-performance edge AI accelerator is proposed in this article. More specifically, the real-time single-channel electroencephalogram (EEG) and electrocardiogram (ECG) signals of the trainees are first collected by the wearable flexible headband and patch. The wavelet packet decomposition algorithm is adopted to decompose and denoise the collected signals. The processed signals are classified by a convolutional neural network model, and an AI accelerator is designed to run this model for portability. The classification results are fed back to the trainees in real-time with a serious game to achieve the closed-loop regulation of their attentions. Finally, a single-blind controlled experiment is conducted to verify the effectiveness of the proposed system. The experimental results indicate that the attention levels of subjects trained with the proposed system are significantly improved (p < 0.05), and the attention-related EEG indicator theta/beta ratio decreased by an average of 21.85%.