分析下这段代码：from mne import Epochs, pick_types, events_from_annotations from mne.io import concatenate_raws from mne.io import read_raw_edf from mne.datasets import eegbci import mne import numpy as np import pandas as pd import glob import numpy as np import os from scipy import signal, fft import matplotlib.pyplot as plt path_time = "ttt.csv" # 患者发病发病起止时间表 file_dir = "chb01" path_save = "data" # 选择患者共有的通道 ch = ['FP1-F7', 'F7-T7', 'T7-P7', 'P7-O1', 'FP1-F3', 'F3-C3', 'C3-P3', 'P3-O1', 'FP2-F4', 'F4-C4', 'C4-P4', 'P4-O2', 'FP2-F8', 'F8-T8', 'T8-P8-0', 'P8-O2', 'FZ-CZ', 'CZ-PZ', 'P7-T7', 'T7-FT9', 'FT9-FT10', 'FT10-T8'] sfreq = 256 bandFreqs = [ {'name': 'Delta', 'fmin': 1, 'fmax': 3}, {'name': 'Theta', 'fmin': 4, 'fmax': 7}, {'name': 'Alpha', 'fmin': 8, 'fmax': 13}, {'name': 'Beta', 'fmin': 14, 'fmax': 31}, {'name': 'Gamma', 'fmin': 31, 'fmax': 40} ] # 定义STFT函数 def STFT(epochsData, sfreq, band=bandFreqs): f, t, Zxx = signal.stft(epochsData, fs=sfreq) bandResult = [] for iter_freq in band: index = np.where((iter_freq['fmin'] < f) & (f < iter_freq['fmax'])) portion = np.zeros(Zxx.shape, dtype=np.complex_) portion[:, :, index, :] = Zxx[:, :, index, :] _, xrec = signal.istft(portion, fs=sfreq) # 保存滤波后的结果 bandResult.append(xrec) return bandResult time = pd.read_csv(path_time,index_col="chb") files = sorted(os.listdir(file_dir)) for file in files: if os.path.splitext(file)[1] == '.edf': f = os.path.splitext(file)[0] f_str = str(os.path.splitext(os.path.splitext(file)[0])[0]) if i == 0: raws = mne.io.read_raw_edf(file_dir+"/" + file,preload=True,verbose=False) raws.pick_channels(ch) raws.filter(0.1,50.,method='iir') raw_d,raw_t = raws[:,:] i+=1 else: i+=1 if f_str in time.index: time.loc[f_str]['start'] = time.loc[f_str]['start'] * 256 + len(raw_t) time.loc[f_str]['end'] = time.loc[f_str]['end']*256 + len(raw_t) raw = mne.io.read_raw_edf(file_dir+"/" + file, preload=True,verbose=False) raw.pick_channels(ch) raw.filter(0.1,50.,method='iir') raws = concatenate_raws([raws,raw]) raws_d, raw_t = raws[:,:] d, t = raws[:,:] data = d*1e6 stft = STFT(d, sfreq) pointNum = d.shape[0] stftFreq = np.abs(fft.fft(stft[:pointNum])) data = np.transpose(stftFreq, axes=(1,3,2,0)) np.save(path_save+"/"+file_dir+".npy",data)

这段代码主要是导入了需要用到的一些Python库和函数，并定义了一个字符串变量path_time。其中，import语句用于导入特定的Python库和其函数或变量；from语句用于从Python库中导入特定的函数或变量；字符串变量path_time用于存储CSV文件的路径。此外，这段代码还没有执行任何具体的操作或功能，只是声明了准备要使用的工具。

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介绍一下这段代码的Depthwise卷积层def get_data4EEGNet(kernels, chans, samples): K.set_image_data_format('channels_last') data_path = '/Users/Administrator/Desktop/project 5-5-1/' raw_fname = data_path + 'concatenated.fif' event_fname = data_path + 'concatenated.fif' tmin, tmax = -0.5, 0.5 #event_id = dict(aud_l=769, aud_r=770, foot=771, tongue=772) raw = io.Raw(raw_fname, preload=True, verbose=False) raw.filter(2, None, method='iir') events, event_id = mne.events_from_annotations(raw, event_id={'769': 1, '770': 2,'770': 3, '771': 4}) #raw.info['bads'] = ['MEG 2443'] picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False) epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False, picks=picks, baseline=None, preload=True, verbose=False) labels = epochs.events[:, -1] print(len(labels)) print(len(epochs)) #epochs.plot(block=True) X = epochs.get_data() * 250 y = labels X_train = X[0:144,] Y_train = y[0:144] X_validate = X[144:216, ] Y_validate = y[144:216] X_test = X[216:, ] Y_test = y[216:] Y_train = np_utils.to_categorical(Y_train - 1) Y_validate = np_utils.to_categorical(Y_validate - 1) Y_test = np_utils.to_categorical(Y_test - 1) X_train = X_train.reshape(X_train.shape[0], chans, samples, kernels) X_validate = X_validate.reshape(X_validate.shape[0], chans, samples, kernels) X_test = X_test.reshape(X_test.shape[0], chans, samples, kernels) return X_train, X_validate, X_test, Y_train, Y_validate, Y_test kernels, chans, samples = 1, 3, 251 X_train, X_validate, X_test, Y_train, Y_validate, Y_test = get_data4EEGNet(kernels, chans, samples) model = EEGNet(nb_classes=3, Chans=chans, Samples=samples, dropoutRate=0.5, kernLength=32, F1=8, D=2, F2=16, dropoutType='Dropout') model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) checkpointer = ModelCheckpoint(filepath='/Users/XXX/baseline.h5', verbose=1, save_best_only=True) class_weights = {0: 1, 1: 1, 2: 1, 3: 1} fittedModel = model.fit(X_train, Y_train, batch_size=2, epochs=100, verbose=2, validation_data=(X_validate, Y_validate), callbacks=[checkpointer], class_weight=class_weights) probs = model.predict(X_test) preds = probs.argmax(axis=-1) acc = np.mean(preds == Y_test.argmax(axis=-1)) print("Classification accuracy: %f " % (acc))

这段代码是用于对EEG数据进行分类的。首先，它读取了一个数据集，将数据集分成训练集、验证集和测试集。然后，它使用EEGNet模型对数据进行训练和验证，并输出分类准确率。其中，EEGNet模型是一种针对EEG数据设计的卷积神经网络模型，它包括了深度可分离卷积层、批归一化层和Dense层。这个模型的输入是EEG信号，输出是分类结果。在训练过程中，该代码使用了dropout技术和class weight调节技术，以提高模型的鲁棒性和泛化性能。最后，该代码输出了测试集上的分类准确率。

运行代码： import scipy.io import mne from mne.bem import make_watershed_bem import random import string # 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'] subjects_dir = 'E:\MATLABproject\data\MRI\Visit1_040318' subject = ''.join(random.choices(string.ascii_uppercase + string.ascii_lowercase, k=8)) # Prepare surfaces for MNE # Prepare surfaces for MNE surfs = [ mne.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=[0.01], subjects_dir=subjects_dir), # brain mne.make_bem_model(outer_skull_vertices, outer_skull_triangles, conductivity=[0.016], subjects_dir=subjects_dir), # skull mne.make_bem_model(scalp_vertices, scalp_triangles, conductivity=[0.33], subjects_dir=subjects_dir), # skin ] # Create BEM solution model = make_watershed_bem(surfs) solution = mne.make_bem_solution(model) 时报错： Traceback (most recent call last): File "E:\pythonProject\MEG\头模型.py", line 30, in <module> mne.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=[0.01], subjects_dir=subjects_dir), # brain File "<decorator-gen-68>", line 12, in make_bem_model File "E:\anaconda\envs\pythonProject\lib\site-packages\mne\bem.py", line 712, in make_bem_model subject_dir = op.join(subjects_dir, subject) File "E:\anaconda\envs\pythonProject\lib\ntpath.py", line 117, in join genericpath._check_arg_types('join', path, *paths) File "E:\anaconda\envs\pythonProject\lib\genericpath.py", line 152, in _check_arg_types raise TypeError(f'{funcname}() argument must be str, bytes, or ' TypeError: join() argument must be str, bytes, or os.PathLike object, not 'ndarray' 进程已结束,退出代码1

根据错误信息显示，`join()` 函数要求的参数必须是字符串、字节或 os.PathLike 对象，但你传递的是一个 ndarray 对象。

这个问题可能是因为 `subjects_dir` 参数的值不正确。请确保你在调用 `mne.make_bem_model` 函数时传递了正确的 `subjects_dir` 参数。

另外，你在代码中随机生成了一个 `subject` 变量，但它没有在 `mne.make_bem_model` 函数中使用。如果你希望将随机生成的 `subject` 作为参数传递给函数，你需要修改相应的代码。

例如：

import os

# ...

subject = ''.join(random.choices(string.ascii_uppercase + string.ascii_lowercase, k=8))
subjects_dir = 'E:\MATLABproject\data\MRI\Visit1_040318'

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

# ...

在这个示例中，我假设你的 `subjects_dir` 是 `'E:\MATLABproject\data\MRI\Visit1_040318'`，并将随机生成的 `subject` 作为参数传递给 `mne.make_bem_model` 函数。请根据你的实际情况修改 `subjects_dir` 的值。

希望这次能解决你的问题！如果还有其他疑问，请随时提问。