Attention and working memory, which are two fundamental components of cognitive basis, can be improved through cognitive training. In addition, thanks to neuroplasticity, neurons are able to adapt quickly to the demands placed on them. By developing new neural networks and strengthening important connections, a cognitive training program can measurably and permanently improve brain activity. In this paper, we present a concept of AIoT based neural decoding and neurofeedback to accelerate cognitive training, the preliminary results and research directions. The proposed concept is to design adequate tiny machine learning to extract the relevant features and characteristics from physiological signals. A tiny ML performs classification or recognition of relevant patterns, based on which the neurofeedback system is appropriately designed for more effective cognitive training