用自适应机器学习调节脑电波，提升脆性X综合征患者注意力。
Brian Intensify: An Adaptive Machine Learning Framework for Auditory EEG Stimulation and Cognitive Enhancement in FXS
- 根据脑电特征动态调整特定频率的听觉刺激。
- 13Hz刺激使α波增强、γ波抑制，改善认知准备度。
- 适合神经发育障碍康复研究者与脑机接口开发者。
脆性X综合征（FXS）和自闭症谱系障碍（ASD）常伴随皮层振荡异常，尤其在α和γ频段，影响注意力、感官处理及认知功能。本研究提出一种基于机器学习的自适应脑机接口系统，通过特定频率的听觉刺激调节神经振荡，以提升FXS患者的认知准备度。采用128通道脑电设备，在38名参与者中记录数据，实验包含30秒基线期和60秒分别在7Hz、9Hz、11Hz、13Hz下的听觉同步刺激。分析了α、γ、δ、θ、β频段功率谱特征及跨频耦合指标（如α-γ、α-β）。结果发现，峰值α功率、峰值γ功率以及每秒每通道α功率为最具区分性的生物标志物。13Hz刺激条件下，α活动显著增加，γ活动被有效抑制，符合优化目标。构建了监督式机器学习框架，可预测脑电反应并实时动态调整刺激参数，实现个体化自适应。该工作建立了新型脑电驱动的优化框架，为下一代个性化神经调控脑机接口系统提供基础模型。
原文摘要 · Abstract (English)
Neurodevelopmental disorders such as Fragile X Syndrome (FXS) and Autism Spectrum Disorder (ASD) are characterized by disrupted cortical oscillatory activity, particularly in the alpha and gamma frequency bands. These abnormalities are linked to deficits in attention, sensory processing, and cognitive function. In this work, we present an adaptive machine learning-based brain-computer interface (BCI) system designed to modulate neural oscillations through frequency-specific auditory stimulation to enhance cognitive readiness in individuals with FXS. EEG data were recorded from 38 participants using a 128-channel system under a stimulation paradigm consisting of a 30-second baseline (no stimulus) followed by 60-second auditory entrainment episodes at 7Hz, 9Hz, 11Hz, and 13Hz. A comprehensive analysis of power spectral features (Alpha, Gamma, Delta, Theta, Beta) and cross-frequency coupling metrics (Alpha-Gamma, Alpha-Beta, etc.) was conducted. The results identified Peak Alpha Power, Peak Gamma Power, and Alpha Power per second per channel as the most discriminative biomarkers. The 13Hz stimulation condition consistently elicited a significant increase in Alpha activity and suppression of Gamma activity, aligning with our optimization objective. A supervised machine learning framework was developed to predict EEG responses and dynamically adjust stimulation parameters, enabling real-time, subject-specific adaptation. This work establishes a novel EEG-driven optimization framework for cognitive neuromodulation, providing a foundational model for next-generation AI-integrated BCI systems aimed at personalized neurorehabilitation in FXS and related disorders.
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