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说明

转到末尾下载完整示例代码。

连续数据回归（rER[P/F]）#

本示例说明 rER[P/F]——即对连续数据进行回归——如何推广传统平均。若所有预处理步骤相同、分段之间不存在重叠，且所有预测变量均为二值，回归结果与传统平均几乎完全一致；若存在重叠或预测变量为连续变量，传统平均不再适用，而回归仍可估计效应，包括连续预测变量的效应。

rERP 的方法见 Smith 与 Kutas [1]。

# Authors: Jona Sassenhagen <jona.sassenhagen@gmail.de>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt

import mne
from mne.datasets import sample
from mne.stats.regression import linear_regression_raw

# Load and preprocess data
data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
raw = mne.io.read_raw_fif(raw_fname)
raw.pick(["grad", "stim"], exclude="bads").load_data()
raw.filter(1, None, fir_design="firwin")  # high-pass

# Set up events
events = mne.find_events(raw)
event_id = {"Aud/L": 1, "Aud/R": 2}
tmin, tmax = -0.1, 0.5

# regular epoching
picks = mne.pick_types(raw.info, meg=True)
epochs = mne.Epochs(
    raw,
    events,
    event_id,
    tmin,
    tmax,
    reject=None,
    baseline=None,
    preload=True,
    verbose=False,
)

# rERF
evokeds = linear_regression_raw(
    raw, events=events, event_id=event_id, reject=None, tmin=tmin, tmax=tmax
)
# linear_regression_raw returns a dict of evokeds
# select conditions similarly to mne.Epochs objects

# plot both results, and their difference
cond = "Aud/L"
fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
params = dict(
    spatial_colors=True, show=False, ylim=dict(grad=(-200, 200)), time_unit="s"
)
epochs[cond].average().plot(axes=ax1, **params)
evokeds[cond].plot(axes=ax2, **params)
contrast = mne.combine_evoked([evokeds[cond], epochs[cond].average()], weights=[1, -1])
contrast.plot(axes=ax3, **params)
ax1.set_title("Traditional averaging")
ax2.set_title("rERF")
ax3.set_title("Difference")
plt.show()
Traditional averaging, rERF, Difference
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.
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Filtering raw data in 1 contiguous segment
Setting up high-pass filter at 1 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal highpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Filter length: 497 samples (3.310 s)

Finding events on: STI 014
319 events found on stim channel STI 014
Event IDs: [ 1  2  3  4  5 32]

脚本总运行时间：（0 分 1.001 秒）

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