脑电分析系列[MNE-Python-4]| MNE中数据结构Evoked及其对象创建

import os.path as op
import matplotlib.pyplot as plt
import mne

"""
从文件中读取诱发数据集
"""
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evokeds = mne.read_evokeds(fname, baseline=(None, 0), proj=True)
print(evokeds)

    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)

evoked = mne.read_evokeds(fname, condition='Left Auditory')
evoked.apply_baseline((None, 0)).apply_proj()
print(evoked)

# 打印evoked的信息，这个信息和Raw对象以及Epochs对象中的info很相似
print(evoked.info)
print(evoked.times)

# 查看evoked结构其他属性
print(evoked.nave)  # Number of averaged epochs.
print(evoked.first)  # First time sample.
print(evoked.last)  # Last time sample.
print(evoked.comment)  # Comment on dataset. Usually the condition.
print(evoked.kind)  # Type of data, either average or standard_error.

55
-120
300
Left Auditory
average

gfp = evoked.copy().pick_types(eeg=True, meg=False).data.std(axis=0)
fig, ax = plt.subplots(1)
ax.plot(evoked.times, gfp / 1e6)  # scale to uV
ax.set(xlabel='Time (sec)', ylabel='GFP (uV)')
fig.tight_layout()

from mne import read_evokeds
from mne.datasets import sample
import matplotlib.pyplot as plt

"""
文件存放地址
"""
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'

"""
读取fif文件:
sample_audvis-ave.fif

只读取左听觉的电位数据
""" 
condition = 'Left Auditory'
evoked = read_evokeds(fname, condition=condition, baseline=(None, 0),
                      proj=True)

    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)

evoked.plot(exclude=[], time_unit='s')
plt.show()

evoked.plot_image(exclude=[], time_unit='s')
plt.show()

import mne
import numpy as np
import matplotlib.pyplot as plt

# 采样频率
sfreq = 100
data = np.random.randn(10, 5, sfreq * 2)

# 创建一个info结构
info = mne.create_info(
    ch_names=['MEG1', 'MEG2', 'EEG1', 'EEG2', 'EOG'],
    ch_types=['grad', 'grad', 'eeg', 'eeg', 'eog'],
    sfreq=sfreq
)

"""
tmin:event开始前的时间，如果未指定，则默认为0
"""
# 设置事件开始前时间为-0.1s
tmin = -0.1

# 对数据求平均
data_evoked = data.mean(0)

# epochs的数量
nave = data.shape[0]

# 给evoked起一个名称
comment = "Smiley faces"

"""
利用mne.EvokedArray创建Evoked对象
"""
evoked_array = mne.EvokedArray(data_evoked, info, tmin,
                               comment=comment, nave=nave)
print(evoked_array)
_ = evoked_array.plot(time_unit='s')

import numpy as np
import neo

import mne
import matplotlib.pyplot as plt

"""
设置event id,用来识别events.
"""
event_id = 1
# 第一列表示样本编号
events = np.array([[200, 0, event_id],
                   [1200, 0, event_id],
                   [2000, 0, event_id]])  # List of three arbitrary events

sfreq = 1000  # 采样频率
times = np.arange(0, 10, 0.001)  # Use 10000 samples (10s)

sin = np.sin(times * 10)  # 乘以 10 缩短周期
cos = np.cos(times * 10)

"""
利用sin和cos创建一个2个通道的700 ms epochs的数据集

只要是(n_epochs, n_channels, n_times)形状的数据，都可以被用来创建
"""
epochs_data = np.array([[sin[:700], cos[:700]],
                        [sin[1000:1700], cos[1000:1700]],
                        [sin[1800:2500], cos[1800:2500]]])

ch_names = ['sin', 'cos']
ch_types = ['mag', 'mag']
info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types

nave = len(epochs_data)  # Number of averaged epochs
evoked_data = np.mean(epochs_data, axis=0)

evokeds = mne.EvokedArray(evoked_data, info=info, tmin=-0.2,
                          comment='Arbitrary', nave=nave)

picks = mne.pick_types(info, meg=True, eeg=False, misc=False)

evokeds.plot(picks=picks, show=True, units={'mag': '-'},
             titles={'mag': 'sin and cos averaged'}, time_unit='s')
plt.show()