Dementia Neurodynamics Eeg

import numpy as np
from scipy import signal

def compute_lyapunov(eeg, m=10, tau=10):
    """计算Lyapunov指数"""
    n = len(eeg)
    embedded = np.array([eeg[i:i+m] for i in range(0, n-m, tau)])
    lyap_sum, count = 0, 0
    for i in range(len(embedded)-1):
        d = np.linalg.norm(embedded - embedded[i], axis=1)
        d[i] = np.inf
        nearest = np.argmin(d)
        if i+1 < len(embedded) and nearest+1 < len(embedded):
            lyap_sum += np.log(d[nearest+1]/d[nearest]+1e-10)
            count += 1
    return lyap_sum/count if count > 0 else 0

def compute_plv(eeg1, eeg2):
    """相位锁定值"""
    p1 = np.angle(signal.hilbert(eeg1))
    p2 = np.angle(signal.hilbert(eeg2))
    return np.abs(np.mean(np.exp(1j*(p1-p2))))

关键发现

• AD：协调性低、连接性低、随机性高
• FTD：协调性保留、额叶慢活动异常
• 两者代表从正常相反方向的偏离

参考文献

• arXiv:2507.08728

Activation Keywords

• dementia-neurodynamics-eeg
• dementia-neurodynamics-eeg 技能
• dementia-neurodynamics-eeg skill

Tools Used

• read - Read documentation and references
• web_search - Search for related information
• web_fetch - Fetch paper or documentation

Instructions for Agents

Follow these steps when applying this skill:

Step 1: 频谱功率：

Step 2: Lyapunov 指数：

Step 3: 相位同步：

Step 4: 功能网络：

Step 5: Understand the Request

Examples

Example 1: Basic Application

User: I need to apply Dementia Neurodynamics EEG - 痴呆症脑网络神经动力学EEG分析 to my analysis.

Agent: I'll help you apply dementia-neurodynamics-eeg. First, let me understand your specific use case...

Context: Apply the methodology

Example 2: Advanced Scenario

User: Complex analysis scenario

Agent: Based on the methodology, I'll guide you through the advanced application...

Example 2: Advanced Application

User: What are the key considerations for dementia-neurodynamics-eeg?

Agent: Let me search for the latest research and best practices...