Intervention of Artificial Intelligence (AI)-based individualized music therapy on emotion and memory function of elderly people with mild cognitive impairment (MCI)

This study proposes an artificial intelligence (AI)-based individualized music therapy system targeting the emotional and memory functions of the elderly with mild cognitive impairment (MCI). The system can real-time identify users’ emotional states and then dynamically adjust music intervention content accordingly. Thus, it achieves emotional regulation and auxiliary improvement of memory functions. The study innovatively proposes an emotion recognition method based on prefrontal electroencephalogram (EEG) and heart rate variability (HRV). The system adopts a feature fusion strategy of 6-lead prefrontal EEG and HRV, and uses the support vector machine (SVM) algorithm to achieve emotion classification. In addition, a closed-loop system of “emotion recognition-music intervention” is constructed, enabling the system to instantly adjust the type and parameters of played music according to the emotional changes of the elderly. In the experiment involving 60 elderly MCI patients, the system can complete high-precision emotion recognition with only six leads, which greatly simplifies the equipment complexity. Results show that the average accuracy of emotion recognition by the proposed method reaches 89.7%, which is significantly higher than 83.3% of the single EEG method and 76.6% of the single HRV method. This study verifies the core role of the prefrontal lobe region in emotional processing and provides an engineering basis for simplifying the emotion recognition system.

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