mne中计算脑电mat文件的Hjorth的函数
时间: 2024-05-12 14:20:19 浏览: 11
以下是使用MNE计算Hjorth函数的示例代码:
```python
import mne
import numpy as np
# 读取EEG数据
raw = mne.io.read_raw_matlab('data.mat', preload=True)
# 选择感兴趣的通道
ch_names = ['FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2']
raw.pick_channels(ch_names)
# 提取EEG数据
data = raw.get_data()
# 计算Hjorth参数
def hjorth(x, dt=1):
"""计算Hjorth参数"""
dx = np.diff(x)
ddx = np.diff(dx)
var_zero = np.var(x)
var_dx = np.var(dx)
var_ddx = np.var(ddx)
activity = var_zero
mobility = np.sqrt(var_dx / var_zero) / dt
complexity = np.sqrt(var_ddx / var_dx) / np.sqrt(var_zero) / dt
return activity, mobility, complexity
# 计算每个通道的Hjorth参数
hjorth_params = []
for i in range(data.shape[0]):
ch_data = data[i]
activity, mobility, complexity = hjorth(ch_data)
hjorth_params.append([activity, mobility, complexity])
# 将结果转换为MNE对象
hjorth_params = np.array(hjorth_params)
info = mne.create_info(ch_names=['activity', 'mobility', 'complexity'], sfreq=raw.info['sfreq'], ch_types=['misc']*3)
hjorth_epochs = mne.EpochsArray(hjorth_params[:, np.newaxis, :], info)
# 可视化结果
hjorth_epochs.plot()
```
在这个例子中,我们首先使用`mne.io.read_raw_matlab`函数读取一个MATLAB格式的EEG数据文件。然后,我们选择感兴趣的通道,提取EEG数据,并计算每个通道的Hjorth参数。最后,我们将结果转换为MNE对象并可视化结果。