Constructing a Personalized Music Prenatal Education System by Utilizing Brain-Computer Interface along with AI Algorithms and Conducting Research on Its Neural Mechanisms

Abstract

This study delved into the construction of a personalized music prenatal education system that combines brain computer interface (BCI) technology with artificial intelligence algorithms, and also examined its potential neural mechanism. The system makes use of BCI to capture the real-time electroencephalogram (EEG) signals of pregnant women, thus allowing for a dynamic analysis of the conditions of both the mother and the fetus. Artificial intelligence algorithms handle these signals to customize music selections, tweaking the rhythm, volume, and genre according to the stage of pregnancy, fetal movements, and the emotional ups and downs of the mother. A modular design brings together hardware components, adaptive algorithms, and interactive interfaces to offer precise auditory stimulation. Experimental verification has shown that the system is effective in strengthening the bond between the mother and the fetus and in promoting neural plasticity, and improvements in the indicators of fetal brain development have been observed. Neurobiological analysis reveals that structural auditory stimulation regulates the activity of neurotransmitters and the formation of synapses during crucial pregnancy periods. This study connects technological innovation with the progress of neuroscience, presenting scalable applications on commercial prenatal care platforms. The limitations involve mitigating signal interference and tracking longitudinal outcomes, which point out the direction for future enhancements in the integration of wearable BCI and the design of multimodal stimulation.