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生成模拟的源数据

本示例说明如何使用 mne.simulation.SourceSimulator 类生成源估计与原始数据。本示例仅作简要介绍，只展示最简单的用法。

# Author: Kostiantyn Maksymenko <kostiantyn.maksymenko@gmail.com>
#         Samuel Deslauriers-Gauthier <sam.deslauriers@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

import mne
from mne.datasets import sample

print(__doc__)

本示例将使用示例受试者的信息；若本机尚无数据会自动下载。我们还会设置受试者目录，避免每次调用函数时重复传入。

data_path = sample.data_path()
subjects_dir = data_path / "subjects"
subject = "sample"

首先，从测试受试者获取 info 结构。

evoked_fname = data_path / "MEG" / subject / "sample_audvis-ave.fif"
info = mne.io.read_info(evoked_fname)
tstep = 1.0 / info["sfreq"]

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

模拟源还需要源空间，可从示例受试者的正向解中获得。

fwd_fname = data_path / "MEG" / subject / "sample_audvis-meg-eeg-oct-6-fwd.fif"
fwd = mne.read_forward_solution(fwd_fname)
src = fwd["src"]

Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523 (FIFF_MNE_FORWARD_SOLUTION_GRAD)) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523 (FIFF_MNE_FORWARD_SOLUTION_GRAD)) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    Forward solutions combined: MEG, EEG
    Source spaces transformed to the forward solution coordinate frame

为选择要激活的区域，我们以尾侧额中回为种子生长出一个感兴趣区域。

selected_label = mne.read_labels_from_annot(
    subject, regexp="caudalmiddlefrontal-lh", subjects_dir=subjects_dir
)[0]
location = "center"  # Use the center of the region as a seed.
extent = 10.0  # Extent in mm of the region.
label = mne.label.select_sources(
    subject, selected_label, location=location, extent=extent, subjects_dir=subjects_dir
)

Reading labels from parcellation...
   read 1 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/lh.aparc.annot
   read 0 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/rh.aparc.annot

为待激活区域中的每个源定义活动的时间过程。这里使用 18 Hz 正弦波，峰值幅度为 10 nAm。

source_time_series = np.sin(2.0 * np.pi * 18.0 * np.arange(100) * tstep) * 10e-9

用事件定义活动发生的时间：第一列是事件所在的采样点，第二列不使用，第三列是事件 ID。这里事件每 200 个采样点出现一次。

n_events = 50
events = np.zeros((n_events, 3), int)
events[:, 0] = 100 + 200 * np.arange(n_events)  # Events sample.
events[:, 2] = 1  # All events have the sample id.

创建模拟的源活动。这里使用 SourceSimulator，其 add_data 方法是关键：它指定事件类型发生的位置（label）、内容（source_time_series）与时间（events）。

source_simulator = mne.simulation.SourceSimulator(src, tstep=tstep)
source_simulator.add_data(label, source_time_series, events)

把源时间序列投影到传感器空间并加入噪声。源模拟器可以直接传给 simulate_raw 函数。

raw = mne.simulation.simulate_raw(info, source_simulator, forward=fwd)
cov = mne.make_ad_hoc_cov(raw.info)
mne.simulation.add_noise(raw, cov, iir_filter=[0.2, -0.2, 0.04], rng=97)
raw.plot()

Setting up raw simulation: 1 position, "cos2" interpolation
Event information stored on channel:              STI 014
    Interval 0.000–1.665 s
Setting up forward solutions
Computing gain matrix for transform #1/1
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    Interval 0.000–1.665 s
    10 STC iterations provided
[done]
Adding noise to 366/376 channels (366 channels in cov)

绘制诱发数据，从另一个角度查看模拟的原始数据。

events = mne.find_events(raw)
epochs = mne.Epochs(raw, events, 1, tmin=-0.05, tmax=0.2)
evoked = epochs.average()
evoked.plot()

Finding events on: STI 014
50 events found on stim channel STI 014
Event IDs: [1]
Not setting metadata
50 matching events found
Setting baseline interval to [-0.049948803289596964, 0.0] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 4)
4 projection items activated