Patients with Alzheimer's disease (AD) frequently present both cognitive decline and emotional dysregulation. Pharmacological treatment may reduce symptom progression, but side effects and long-term cost remain concerns, while traditional non-pharmacological interventions are often limited in personalization and objective evaluation. To address this gap, this paper proposes an emotional-arousal assistance system that combines electroencephalography (EEG) and large language models. The system performs real-time emotion recognition from EEG, generates simplified personalized poetry from autobiographical memory cues, and delivers coordinated multimodal stimulation with soothing music. Experiments on 2,132 EEG samples with six deep-learning models show that LSTM performs best (Accuracy 91.56%, Precision 92.9%, Recall 91.51%, F1-score 91.42%). Pilot testing indicates improved emotional arousal experience. The study provides a practical path toward personalized and quantifiable non-pharmacological intervention for AD.

Zimo Xu. EEG- and Language Generation-Based Emotional Arousal Assistance System for Alzheimer's Disease Patients. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 5: 36-47. https://doi.org/10.25236/AJCIS.2026.090505.

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