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npx versuz@latest install hiyenwong-ai-collection-collection-skills-eeg-brain-connectivity-bcigit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-eeg-brain-connectivity-bci/SKILL.md---
name: eeg-brain-connectivity-bci
description: EEG脑连接BCI分析方法论。通过功能连接分析理解脑网络在BCI中的机制,用于神经康复和外骨骼控制。适用于脑机接口、神经康复、步态训练。触发词:EEG、脑连接、BCI、脑机接口、功能网络、神经康复、brain-computer interface、neurorehabilitation。
user-invocable: true
---
# EEG Brain Connectivity for BCI - EEG脑连接BCI分析
## 核心思想
通过分析EEG脑连接理解功能网络涌现,改进BCI信号分析和分类,用于运动障碍患者的神经康复。
**来源:** arXiv:2007.11674
**效用:** 0.97
---
## 方法论
### 四种策略
| 策略 | 目的 |
|------|------|
| 特征提取 | 用连接特征增强分类 |
| 状态监测 | 跟踪脑网络变化 |
| 适应性控制 | 根据连接状态调整BCI |
| 神经康复 | 结合外骨骼促进恢复 |
### 实现框架
```python
import numpy as np
from scipy import signal
class EEGConnectivityBCI:
"""EEG脑连接BCI分析器"""
def __init__(self, n_channels=32, fs=500):
self.n_channels = n_channels
self.fs = fs
def compute_functional_connectivity(self, eeg_data):
"""
计算功能连接矩阵
Parameters:
-----------
eeg_data : np.ndarray, shape (n_channels, n_samples)
Returns:
--------
fc_matrix : np.ndarray, shape (n_channels, n_channels)
"""
fc_matrix = np.zeros((self.n_channels, self.n_channels))
for i in range(self.n_channels):
for j in range(i+1, self.n_channels):
# 相位锁定值
plv = self._phase_locking_value(eeg_data[i], eeg_data[j])
fc_matrix[i, j] = plv
fc_matrix[j, i] = plv
return fc_matrix
def _phase_locking_value(self, x, y):
"""相位锁定值"""
phase_x = np.angle(signal.hilbert(x))
phase_y = np.angle(signal.hilbert(y))
return np.abs(np.mean(np.exp(1j * (phase_x - phase_y))))
def extract_features(self, fc_matrix):
"""
从连接矩阵提取特征
Returns:
--------
features : dict
"""
# 网络特征
features = {
'mean_connectivity': np.mean(fc_matrix),
'std_connectivity': np.std(fc_matrix),
'clustering': self._clustering_coefficient(fc_matrix),
'path_length': self._average_path_length(fc_matrix)
}
return features
def _clustering_coefficient(self, fc_matrix, threshold=0.5):
"""聚类系数"""
binary = (fc_matrix > threshold).astype(int)
n = len(binary)
clustering = []
for i in range(n):
neighbors = np.where(binary[i] == 1)[0]
if len(neighbors) > 1:
triangles = 0
for j in neighbors:
for k in neighbors:
if binary[j, k]:
triangles += 1
clustering.append(triangles / (len(neighbors) * (len(neighbors) - 1)))
return np.mean(clustering) if clustering else 0
def _average_path_length(self, fc_matrix, threshold=0.5):
"""平均路径长度"""
# 简化实现
binary = (fc_matrix > threshold).astype(int)
return 1.0 / (np.mean(binary) + 1e-10)
```
---
## 应用场景
1. **BCI控制** - 轮椅、外骨骼
2. **神经康复** - 步态训练
3. **状态监测** - 疲劳、注意力
---
## Activation Keywords
- EEG
- 脑连接
- BCI
- 脑机接口
- 功能网络
## Tools Used
- numpy
- scipy
## Instructions for Agents
1. 计算功能连接矩阵
2. 提取网络特征
3. 用于BCI分类或监测
## Examples
分析运动想象任务的EEG连接特征。
## 参考文献
- arXiv:2007.11674