bci competition iV 2a转换成TU Dataset格式python代码
时间: 2023-09-28 19:09:53 浏览: 181
以下是将BCI Competition IV 2a数据集转换为TU Dataset格式的Python代码示例:
```python
import mne
from mne.io import concatenate_raws
from mne.datasets import eegbci
# 设置数据集路径和文件名
data_path = eegbci.load_data(1, [4])[0]
file = 'A01T.gdf'
# 读取GDF文件
raw = mne.io.read_raw_gdf(data_path + file)
# 选择需要的通道
raw.pick_channels(['C3', 'C4', 'Cz', 'Fz', 'P3', 'P4', 'PO7', 'PO8'])
# 设置事件标记
events, _ = mne.events_from_annotations(raw)
# 将数据切割成1秒的小段
epochs = mne.Epochs(raw, events, tmin=0, tmax=1, baseline=None)
# 将数据集保存为TU Dataset格式
epochs.save('converted_dataset.fif')
```
这段代码使用MNE库读取GDF格式的数据并选择需要的通道,然后根据事件标记将数据切割成1秒的小段,最后将数据集以TU Dataset格式保存在本地。您需要根据自己的实际情况对代码进行适当的修改。
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bci competition iV 2a的.mat格式的数据集转换成TU Dataset格式python代码
以下是将BCI Competition IV 2a数据集(.mat格式)转换为TU Dataset格式的Python代码示例:
```python
import scipy.io
import mne
# 设置数据集路径和文件名
data_path = 'path/to/mat/dataset/'
file = 'A01T.mat'
# 读取MAT文件
mat = scipy.io.loadmat(data_path + file)
# 获取EEG数据和事件标记
eeg_data = mat['data'][:-1]
event_data = mat['data'][-1]
# 创建MNE对象
ch_names = ['Fz', 'C3', 'Cz', 'C4', 'Pz', 'PO7', 'PO8', 'Oz']
ch_types = ['eeg'] * 8
info = mne.create_info(ch_names=ch_names, sfreq=250, ch_types=ch_types)
raw = mne.io.RawArray(eeg_data, info)
# 设置事件标记
events = mne.find_events(raw, stim_channel='STI 014')
# 将数据切割成1秒的小段
epochs = mne.Epochs(raw, events, tmin=0, tmax=1, baseline=None)
# 将数据集保存为TU Dataset格式
epochs.save('converted_dataset.fif')
```
这段代码使用SciPy库读取MAT格式的数据,并根据通道信息创建MNE对象。然后,根据事件标记将数据切割成1秒的小段,最后将数据集以TU Dataset格式保存在本地。您需要根据自己的实际情况对代码进行适当的修改。
bci competition iV 2a转换成LegacyTUDataset的python代码
将BCI Competition IV 2a数据集转换为Legacy TU Dataset的Python代码如下:
```python
import numpy as np
import mne
# Load the BCI Competition IV 2a dataset
data_train = np.load('A01T.npz')
data_test = np.load('A01E.npz')
# Extract features and labels from the dataset
X_train = data_train['data']
y_train = data_train['labels']
X_test = data_test['data']
y_test = data_test['labels']
# Reshape the data arrays to match the Legacy TU Dataset format
X_train = X_train.reshape(-1, 22, 1000).transpose(0, 2, 1)
y_train = y_train.reshape(-1)
X_test = X_test.reshape(-1, 22, 1000).transpose(0, 2, 1)
y_test = y_test.reshape(-1)
# Create MNE Raw objects for both the training and testing data
ch_names = ['Fz', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz']
sfreq = 250
info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types='eeg')
raw_train = mne.io.RawArray(X_train, info)
raw_test = mne.io.RawArray(X_test, info)
# Add event markers for the labels in both the training and testing data
events_train = np.vstack((np.arange(len(y_train)), np.zeros(len(y_train)), y_train)).T
events_test = np.vstack((np.arange(len(y_test)), np.zeros(len(y_test)), y_test)).T
raw_train.add_events(events_train, stim_channel='STI 014')
raw_test.add_events(events_test, stim_channel='STI 014')
# Save the MNE Raw objects as .fif files
mne.io.write_raw_fif(raw_train, 'A01T_raw.fif', overwrite=True)
mne.io.write_raw_fif(raw_test, 'A01E_raw.fif', overwrite=True)
```
这段代码假设您已经将BCI Competition IV 2a数据集下载到当前工作目录中,并已将其命名为"A01T.npz"和"A01E.npz"。代码使用NumPy和MNE库来加载、处理和保存数据。它首先从.npz文件中提取数据和标签,并将其转换为符合Legacy TU Dataset格式的形式。然后,代码使用MNE库创建了两个Raw对象,一个用于训练数据,另一个用于测试数据。最后,代码将标签添加为事件标记,并将MNE Raw对象保存为.fif文件。
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