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

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

import numpy as np
import os
from dgl.data.utils import save_graphs

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

    # extract features and labels
    X = data['data']
    y = data['labels'][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_IIa.bin', graphs)

这个代码将 `BCI_Competition_IIa.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_IIa.bin` 中。