注意

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MEG/EEG 分析的概述使用 MNE-Python#

本教程涵盖了事件相关分析的基本 EEG/MEG 流程：加载数据、分段、平均、绘图以及从传感器数据估计皮质活动。它介绍了核心的 MNE-Python 数据结构 Raw、Epochs, Evoked，以及 SourceEstimate，并且相当快地覆盖了广泛的内容（以牺牲深度为代价）。后续教程将更详细地探讨这些主题。

我们首先导入必要的 Python 模块：

# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

import mne

加载数据#

MNE-Python 的数据结构基于 Neuromag 的 FIF 文件格式，但提供了读取多种其他数据格式的函数。MNE-Python 还提供了与多种公开数据集的接口，这些数据集 MNE-Python 可以为你下载和管理。

本教程将从加载一个示例数据集（名为“Sample”）开始，该数据集包含一名受试者在进行视听实验时采集的 EEG 和 MEG 数据，以及该受试者的结构 MRI 扫描。使用 mne.datasets.sample.data_path 函数会自动若数据集不在预期位置，则会自动下载该数据集，然后返回数据集的目录路径（参见文档） data_path 用于列出它检查的目录（在下载前），请注意，为了让本教程在我们的服务器上顺利运行，我们使用了一个经过滤波和降采样的数据版本（ sample_audvis_filt-0-40_raw.fif ），但未滤波的版本（sample_audvis_raw.fif）也包含在样本数据集中，在本地运行教程时可以替换使用。

sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = (
    sample_data_folder / "MEG" / "sample" / "sample_audvis_filt-0-40_raw.fif"
)
raw = mne.io.read_raw_fif(sample_data_raw_file)

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.

默认情况下，read_raw_fif 会显示它正在加载的文件的一些信息；例如，这里它告诉我们文件中除了记录的数据外，还有四个“投影项”；这些是 SSP 投影器，用于从 MEG 信号中去除环境噪声，以及一个用于对 EEG 通道进行均值参考的投影器；这些内容在教程投影器和投影的背景知识中有讨论。除了加载时显示的信息外，你可以通过打印来快速了解 Raw 对象的基本细节；通过打印它的 info 属性（一个类似字典的对象 ，该对象在 Raw、Epochs 和 Evoked 对象之间保持不变）。 info 数据结构用于记录通道位置、应用过的滤波器、投影器等信息。特别要注意 chs 这一项，它表明 MNE-Python 能够检测不同类型的传感器并对其进行适当处理。参见 The Info 数据结构了解更多关于 Info 类的内容。

print(raw)
print(raw.info)

<Raw | sample_audvis_filt-0-40_raw.fif, 376 x 41700 (277.7 s), ~3.2 MiB, data not loaded>
<Info | 14 non-empty values
 bads: 2 items (MEG 2443, EEG 053)
 ch_names: MEG 0113, MEG 0112, MEG 0111, MEG 0122, MEG 0123, MEG 0121, MEG ...
 chs: 204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG
 custom_ref_applied: False
 dev_head_t: MEG device -> head transform
 dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra)
 highpass: 0.1 Hz
 hpi_meas: 1 item (list)
 hpi_results: 1 item (list)
 lowpass: 40.0 Hz
 meas_date: 2002-12-03 19:01:10 UTC
 meas_id: 4 items (dict)
 nchan: 376
 projs: PCA-v1: off, PCA-v2: off, PCA-v3: off, Average EEG reference: off
 sfreq: 150.2 Hz
>

Raw 对象也包含多种内置绘图方法；这里我们展示每种传感器类型的功率谱密度（PSD），使用 compute_psd，以及使用 plot 绘制原始传感器轨迹。在 PSD 图中，我们仅绘制 50 Hz 以下的频率（因为我们的数据在 40 Hz 处进行了低通滤波）。在交互式 Python 会话中，plot 是交互式的，允许滚动、缩放、标记坏通道、注释、切换投影器等。

raw.compute_psd(fmax=50).plot(picks="data", exclude="bads", amplitude=False)
raw.plot(duration=5, n_channels=30)

Effective window size : 13.639 (s)
Plotting power spectral density (dB=True).
Using qt as 2D backend.

Preprocessing#

MNE-Python supports a variety of preprocessing approaches and techniques (maxwell filtering, signal-space projection, independent components analysis, filtering, downsampling, etc); see the full list of capabilities in the mne.preprocessing and mne.filter submodules. Here we’ll clean up our data by performing independent components analysis (ICA); for brevity we’ll skip the steps that helped us determined which components best capture the artifacts (see Repairing artifacts with ICA for a detailed walk-through of that process).

# set up and fit the ICA
ica = mne.preprocessing.ICA(n_components=20, random_state=97, max_iter=800)
ica.fit(raw)
ica.exclude = [1, 2]  # details on how we picked these are omitted here
ica.plot_properties(raw, picks=ica.exclude)

Fitting ICA to data using 364 channels (please be patient, this may take a while)
Selecting by number: 20 components
Fitting ICA took 3.1s.
    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
138 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
138 matching events found
No baseline correction applied
0 projection items activated

Once we’re confident about which component(s) we want to remove, we pass them as the exclude parameter and then apply the ICA to the raw signal. The apply method requires the raw data to be loaded into memory (by default it’s only read from disk as-needed), so we’ll use load_data first. We’ll also make a copy of the Raw object so we can compare the signal before and after artifact removal side-by-side:

orig_raw = raw.copy()
raw.load_data()
ica.apply(raw)

# show some frontal channels to clearly illustrate the artifact removal
chs = [
    "MEG 0111",
    "MEG 0121",
    "MEG 0131",
    "MEG 0211",
    "MEG 0221",
    "MEG 0231",
    "MEG 0311",
    "MEG 0321",
    "MEG 0331",
    "MEG 1511",
    "MEG 1521",
    "MEG 1531",
    "EEG 001",
    "EEG 002",
    "EEG 003",
    "EEG 004",
    "EEG 005",
    "EEG 006",
    "EEG 007",
    "EEG 008",
]
chan_idxs = [raw.ch_names.index(ch) for ch in chs]
orig_raw.plot(order=chan_idxs, start=12, duration=4)
raw.plot(order=chan_idxs, start=12, duration=4)

Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Applying ICA to Raw instance
    Transforming to ICA space (20 components)
    Zeroing out 2 ICA components
    Projecting back using 364 PCA components

Detecting experimental events#

The sample dataset includes several “STIM” channels that recorded electrical signals sent from the stimulus delivery computer (as brief DC shifts / squarewave pulses). These pulses (often called “triggers”) are used in this dataset to mark experimental events: stimulus onset, stimulus type, and participant response (button press). The individual STIM channels are combined onto a single channel, in such a way that voltage levels on that channel can be unambiguously decoded as a particular event type. On older Neuromag systems (such as that used to record the sample data) this summation channel was called STI 014, so we can pass that channel name to the mne.find_events function to recover the timing and identity of the stimulus events.

events = mne.find_events(raw, stim_channel="STI 014")
print(events[:5])  # show the first 5

319 events found on stim channel STI 014
Event IDs: [ 1  2  3  4  5 32]
[[6994    0    2]
 [7086    0    3]
 [7192    0    1]
 [7304    0    4]
 [7413    0    2]]

The resulting events array is an ordinary 3-column NumPy array, with sample number in the first column and integer event ID in the last column; the middle column is usually ignored. Rather than keeping track of integer event IDs, we can provide an event dictionary that maps the integer IDs to experimental conditions or events. In this dataset, the mapping looks like this:

Event ID	Condition
1	auditory stimulus (tone) to the left ear
2	auditory stimulus (tone) to the right ear
3	visual stimulus (checkerboard) to the left visual field
4	visual stimulus (checkerboard) to the right visual field
5	smiley face (catch trial)
32	subject button press

event_dict = {
    "auditory/left": 1,
    "auditory/right": 2,
    "visual/left": 3,
    "visual/right": 4,
    "smiley": 5,
    "buttonpress": 32,
}

Event dictionaries like this one are used when extracting epochs from continuous data; the / character in the dictionary keys allows pooling across conditions by requesting partial condition descriptors (i.e., requesting 'auditory' will select all epochs with Event IDs 1 and 2; requesting 'left' will select all epochs with Event IDs 1 and 3). An example of this is shown in the next section. There is also a convenient plot_events function for visualizing the distribution of events across the duration of the recording (to make sure event detection worked as expected). Here we will also make use of the Info attribute to get the sampling frequency of the recording (so our x-axis will be in seconds instead of in samples).

fig = mne.viz.plot_events(
    events, event_id=event_dict, sfreq=raw.info["sfreq"], first_samp=raw.first_samp
)

For paradigms that are not event-related (e.g., analysis of resting-state data), you can extract regularly spaced (possibly overlapping) spans of data by creating events using mne.make_fixed_length_events and then proceeding with epoching as described in the next section.

Epoching continuous data#

The Raw object and the events array are the bare minimum needed to create an Epochs object, which we create with the Epochs class constructor. Here we’ll also specify some data quality constraints: we’ll reject any epoch where peak-to-peak signal amplitude is beyond reasonable limits for that channel type. This is done with a rejection dictionary; you may include or omit thresholds for any of the channel types present in your data. The values given here are reasonable for this particular dataset, but may need to be adapted for different hardware or recording conditions. For a more automated approach, consider using the autoreject package.

reject_criteria = dict(
    mag=4000e-15,  # 4000 fT
    grad=4000e-13,  # 4000 fT/cm
    eeg=150e-6,  # 150 µV
    eog=250e-6,
)  # 250 µV

We’ll also pass the event dictionary as the event_id parameter (so we can work with easy-to-pool event labels instead of the integer event IDs), and specify tmin and tmax (the time relative to each event at which to start and end each epoch). As mentioned above, by default Raw and Epochs data aren’t loaded into memory (they’re accessed from disk only when needed), but here we’ll force loading into memory using the preload=True parameter so that we can see the results of the rejection criteria being applied:

epochs = mne.Epochs(
    raw,
    events,
    event_id=event_dict,
    tmin=-0.2,
    tmax=0.5,
    reject=reject_criteria,
    preload=True,
)

Not setting metadata
319 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 4)
4 projection items activated
Using data from preloaded Raw for 319 events and 106 original time points ...
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on MAG : ['MEG 1711']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on MAG : ['MEG 1711']
    Rejecting  epoch based on EEG : ['EEG 008']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
10 bad epochs dropped

Next we’ll pool across left/right stimulus presentations so we can compare auditory versus visual responses. To avoid biasing our signals to the left or right, we’ll use equalize_event_counts first to randomly sample epochs from each condition to match the number of epochs present in the condition with the fewest good epochs.

conds_we_care_about = ["auditory/left", "auditory/right", "visual/left", "visual/right"]
epochs.equalize_event_counts(conds_we_care_about)  # this operates in-place
aud_epochs = epochs["auditory"]
vis_epochs = epochs["visual"]
del raw, epochs  # free up memory

Dropped 7 epochs: 121, 195, 258, 271, 273, 274, 275

Like Raw objects, Epochs objects also have a number of built-in plotting methods. One is plot_image, which shows each epoch as one row of an image map, with color representing signal magnitude; the average evoked response and the sensor location are shown below the image:

aud_epochs.plot_image(picks=["MEG 1332", "EEG 021"])

Not setting metadata
136 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
136 matching events found
No baseline correction applied
0 projection items activated

Note

Both Raw and Epochs objects have get_data methods that return the underlying data as a NumPy array. Both methods have a picks parameter for subselecting which channel(s) to return; raw.get_data() has additional parameters for restricting the time domain. The resulting matrices have dimension (n_channels, n_times) for Raw and (n_epochs, n_channels, n_times) for Epochs.

Time-frequency analysis#

The mne.time_frequency submodule provides implementations of several algorithms to compute time-frequency representations, power spectral density, and cross-spectral density. Here, for example, we’ll compute for the auditory epochs the induced power at different frequencies and times, using Morlet wavelets. On this dataset the result is not especially informative (it just shows the evoked “auditory N100” response); see here for a more extended example on a dataset with richer frequency content.

frequencies = np.arange(7, 30, 3)
power = aud_epochs.compute_tfr(
    "morlet", n_cycles=2, return_itc=False, freqs=frequencies, decim=3, average=True
)
power.plot(["MEG 1332"])

[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.5s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    1.9s
[Parallel(n_jobs=1)]: Done 161 tasks      | elapsed:    3.5s
[Parallel(n_jobs=1)]: Done 287 tasks      | elapsed:    5.5s
No baseline correction applied

Estimating evoked responses#

Now that we have our conditions in aud_epochs and vis_epochs, we can get an estimate of evoked responses to auditory versus visual stimuli by averaging together the epochs in each condition. This is as simple as calling the average method on the Epochs object, and then using a function from the mne.viz module to compare the global field power for each sensor type of the two Evoked objects:

aud_evoked = aud_epochs.average()
vis_evoked = vis_epochs.average()

mne.viz.plot_compare_evokeds(
    dict(auditory=aud_evoked, visual=vis_evoked),
    legend="upper left",
    show_sensors="upper right",
)

Multiple channel types selected, returning one figure per type.
combining channels using RMS (grad channels)
combining channels using RMS (grad channels)
combining channels using RMS (mag channels)
combining channels using RMS (mag channels)
combining channels using GFP (eeg channels)
combining channels using GFP (eeg channels)

We can also get a more detailed view of each Evoked object using other plotting methods such as plot_joint or plot_topomap. Here we’ll examine just the EEG channels, and see the classic auditory evoked N100-P200 pattern over dorso-frontal electrodes, then plot scalp topographies at some additional arbitrary times:

aud_evoked.plot_joint(picks="eeg")
aud_evoked.plot_topomap(times=[0.0, 0.08, 0.1, 0.12, 0.2], ch_type="eeg")

Projections have already been applied. Setting proj attribute to True.

Evoked objects can also be combined to show contrasts between conditions, using the mne.combine_evoked function. A simple difference can be generated by passing weights=[1, -1]. We’ll then plot the difference wave at each sensor using plot_topo:

evoked_diff = mne.combine_evoked([aud_evoked, vis_evoked], weights=[1, -1])
evoked_diff.pick(picks="mag").plot_topo(color="r", legend=False)

Inverse modeling#

Finally, we can estimate the origins of the evoked activity by projecting the sensor data into this subject’s source space (a set of points either on the cortical surface or within the cortical volume of that subject, as estimated by structural MRI scans). MNE-Python supports lots of ways of doing this (dynamic statistical parametric mapping, dipole fitting, beamformers, etc.); here we’ll use minimum-norm estimation (MNE) to generate a continuous map of activation constrained to the cortical surface. MNE uses a linear inverse operator to project EEG+MEG sensor measurements into the source space. The inverse operator is computed from the forward solution for this subject and an estimate of the covariance of sensor measurements. For this tutorial we’ll skip those computational steps and load a pre-computed inverse operator from disk (it’s included with the sample data). Because this “inverse problem” is underdetermined (there is no unique solution), here we further constrain the solution by providing a regularization parameter specifying the relative smoothness of the current estimates in terms of a signal-to-noise ratio (where “noise” here is akin to baseline activity level across all of cortex).

# load inverse operator
inverse_operator_file = (
    sample_data_folder / "MEG" / "sample" / "sample_audvis-meg-oct-6-meg-inv.fif"
)
inv_operator = mne.minimum_norm.read_inverse_operator(inverse_operator_file)
# set signal-to-noise ratio (SNR) to compute regularization parameter (λ²)
snr = 3.0
lambda2 = 1.0 / snr**2
# generate the source time course (STC)
stc = mne.minimum_norm.apply_inverse(
    vis_evoked, inv_operator, lambda2=lambda2, method="MNE"
)  # or dSPM, sLORETA, eLORETA

Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
    Reading inverse operator info...
    [done]
    Reading inverse operator decomposition...
    [done]
    305 x 305 full covariance (kind = 1) found.
    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
    Noise covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 2) found.
    Source covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 6) found.
    Orientation priors read.
    22494 x 22494 diagonal covariance (kind = 5) found.
    Depth priors read.
    Did not find the desired covariance matrix (kind = 3)
    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
    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
    Source spaces transformed to the inverse solution coordinate frame
Preparing the inverse operator for use...
    Scaled noise and source covariance from nave = 1 to nave = 136
    Created the regularized inverter
    Created an SSP operator (subspace dimension = 3)
    Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
Applying inverse operator to "0.50 × visual/left + 0.50 × visual/right"...
    Picked 305 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    Computing residual...
    Explained  70.2% variance
    Combining the current components...
[done]

Finally, in order to plot the source estimate on the subject’s cortical surface we’ll also need the path to the sample subject’s structural MRI files (the subjects_dir):

# path to subjects' MRI files
subjects_dir = sample_data_folder / "subjects"
# plot the STC
stc.plot(
    initial_time=0.1, hemi="split", views=["lat", "med"], subjects_dir=subjects_dir
)

Using control points [8.61922423e-11 1.06837855e-10 4.49139511e-10]

The remaining tutorials have much more detail on each of these topics (as well as many other capabilities of MNE-Python not mentioned here: connectivity analysis, encoding/decoding models, lots more visualization options, etc). Read on to learn more!

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