解释new_events = mne.make_fixed_length_events(raw, start=20, stop=70, duration=10.)

这行代码使用MNE-Python库中的`make_fixed_length_events`函数从连续的原始数据中提取事件。其中，`raw`是原始数据，`start`和`stop`是开始和结束的时间（单位为秒），`duration`是每个事件的持续时间（单位为秒）。该函数将原始数据分成多个固定长度的时间段，并将每个时间段视为一个事件。返回的`new_events`是一个包含每个事件的时间戳和事件ID的NumPy数组。

相关问题

import scipy.io import mne from mne.bem import make_watershed_bem # Load .mat files inner_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.inner_skull.mat') outer_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.outer_skull.mat') scalp = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.scalp.mat') print(inner_skull.keys()) # Assuming these .mat files contain triangulated surfaces, we will extract vertices and triangles # This might need adjustment based on the actual structure of your .mat files inner_skull_vertices = inner_skull['Vertices'] inner_skull_triangles = inner_skull['Faces'] outer_skull_vertices = outer_skull['Vertices'] outer_skull_triangles = outer_skull['Faces'] scalp_vertices = scalp['Vertices'] scalp_triangles = scalp['Faces'] # Prepare surfaces for MNE surfs = [ mne.bem.BEMSurface(inner_skull_vertices, inner_skull_triangles, sigma=0.01, id=4), # brain mne.bem.BEMSurface(outer_skull_vertices, outer_skull_triangles, sigma=0.016, id=3), # skull mne.bem.BEMSurface(scalp_vertices, scalp_triangles, sigma=0.33, id=5), # skin ] # Create BEM model model = mne.bem.BEM(surfs, conductivity=[0.3, 0.006, 0.3], is_sphere=False) model.plot(show=False) # Create BEM solution solution = mne.make_bem_solution(model) 运行代码时报错; Traceback (most recent call last): File "E:\pythonProject\MEG\头模型.py", line 24, in <module> mne.bem.BEMSurface(inner_skull_vertices, inner_skull_triangles, sigma=0.01, id=4), # brain AttributeError: module 'mne.bem' has no attribute 'BEMSurface'

根据你的代码和报错信息来看，mne 模块确实没有 `BEMSurface` 这个属性。根据官方文档，在 mne 的最新版本中，使用 `BEMSurface` 已经被废弃，而改用了 `make_watershed_bem` 函数来创建 BEM 模型。

你可以尝试修改你的代码如下：

import scipy.io
import mne
from mne.bem import make_watershed_bem

# Load .mat files
inner_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.inner_skull.mat')
outer_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.outer_skull.mat')
scalp = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.scalp.mat')
print(inner_skull.keys())

# Assuming these .mat files contain triangulated surfaces, we will extract vertices and triangles
# This might need adjustment based on the actual structure of your .mat files

inner_skull_vertices = inner_skull['Vertices']
inner_skull_triangles = inner_skull['Faces']

outer_skull_vertices = outer_skull['Vertices']
outer_skull_triangles = outer_skull['Faces']

scalp_vertices = scalp['Vertices']
scalp_triangles = scalp['Faces']

# Prepare surfaces for MNE
surfs = [
    mne.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=0.01),  # brain
    mne.make_bem_model(outer_skull_vertices, outer_skull_triangles, conductivity=0.016),  # skull
    mne.make_bem_model(scalp_vertices, scalp_triangles, conductivity=0.33),  # skin
]

# Create BEM solution
model = make_watershed_bem(surfs)
solution = mne.make_bem_solution(model)

请注意，我还对 `make_bem_model` 的调用进行了修改，以适应新的 API。希望这能解决你的问题！如果还有其他疑问，请随时提问。

如果使用data_path = mne.datasets.ssvep.data_path()这个数据集来进行以上处理呢，请生成代码

以下是使用MNE库加载SSVEP数据集并进行预处理的示例代码：

import mne

# Load SSVEP dataset
data_path = mne.datasets.ssvep.data_path()
raw = mne.io.read_raw_edf(data_path + '/sub-01/ses-test/eeg/sub-01_ses-test_task-ssvep_eeg.edf', preload=True)

# Set channel types and montage
raw.set_channel_types({'HEOG': 'eog', 'VEOG': 'eog'})
montage = mne.channels.make_standard_montage('standard_1005')
raw.set_montage(montage)

# Apply bandpass filter
raw.filter(1, 40)

# Apply notch filter to remove line noise at 50 Hz
raw.notch_filter(50)

# Apply ICA to remove eye blinks and other artifacts
ica = mne.preprocessing.ICA(n_components=20, random_state=0)
ica.fit(raw)
raw = ica.apply(raw)

# Epoch data and apply baseline correction
events, event_id = mne.events_from_annotations(raw)
epochs = mne.Epochs(raw, events, event_id, tmin=0, tmax=4, baseline=(None, 0), preload=True)

# Average across trials
evoked = epochs.average()

# Plot evoked response
evoked.plot()