bci competition iv 2a

BCI竞赛IV 2A是一项脑机接口竞赛，旨在通过使用脑电信号控制机器人的运动来促进脑机接口技术的发展。该竞赛要求参赛者使用他们的脑电信号来控制机器人的移动，包括向前、向后、向左和向右。这项竞赛旨在推动脑机接口技术的发展，为未来的医疗和工业应用打下基础。

相关问题

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：

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。

BCI competition iv 2a怎么转换成DGLBuiltinDataset格式，python代码

将BCI competition iv 2a数据集转换为DGLBuiltinDataset格式的Python代码如下：

import numpy as np
import os
import scipy.io
import torch
import dgl
from dgl.data.utils import save_graphs

def load_data():
    # load data from BCI competition iv 2a
    data_path = 'A02T.mat'
    data = scipy.io.loadmat(data_path)

    # extract features and labels
    X = data['data']
    y = data['label'][0]

    # convert labels to binary classification
    y[y == -1] = 0

    # build graph for each trial
    graphs = []
    for i in range(X.shape[2]):
        # create graph
        g = dgl.DGLGraph()

        # add nodes
        g.add_nodes(X.shape[1])

        # add edges
        src, dst = np.meshgrid(np.arange(X.shape[1]), np.arange(X.shape[1]))
        src = src.reshape(-1)
        dst = dst.reshape(-1)
        g.add_edges(src, dst)

        # add features and labels
        g.ndata['feat'] = torch.tensor(X[:, :, i].T, dtype=torch.float32)
        g.ndata['label'] = torch.tensor(y[i], dtype=torch.long)

        # add graph to list
        graphs.append(g)

    return graphs

if __name__ == '__main__':
    graphs = load_data()
    save_graphs('BCI_Competition_IV_2a.bin', graphs)

这个代码将 `A02T.mat` 文件中的数据加载到内存中，并将其转换为一个包含多个DGL图的列表。每个DGL图对应于一个试验，其中每个节点都包含一个特征向量和一个标签。最后，它将DGL图列表保存到二进制文件 `BCI_Competition_IV_2a.bin` 中。