|
|
@@ -9,22 +9,24 @@ Download the files from here:
|
|
|
https://www.dropbox.com/sh/vgxed0kx0b31aiv/AAAS2E7qXpn0vPjqjMLVW4ZOa?dl=0
|
|
|
|
|
|
|
|
|
-## Usage of this dataset
|
|
|
+## Loading this dataset
|
|
|
|
|
|
The braindecode toolbox at https://github.com/robintibor/braindecode provides code to load this dataset in python.
|
|
|
-You can run the following code to get an MNE RawArray:
|
|
|
+You can run the following code to get an [MNE RawArray](https://mne-tools.github.io/stable/generated/mne.io.RawArray.html):
|
|
|
|
|
|
```python
|
|
|
from braindecode.datasets.bbci import BBCIDataset
|
|
|
cnt = BBCIDataset(filename='./test/1.mat', load_sensor_names=None).load()
|
|
|
```
|
|
|
+For using the dataset for decoding, see the next section.
|
|
|
|
|
|
-The `example.py` code in this repository shows how to reproduce the decoding results from the paper above.
|
|
|
+## Reproduction of our results
|
|
|
+The `example.py` code in this repository shows how to reproduce the decoding results from the paper above and can also be used as an example code for decoding.
|
|
|
|
|
|
|
|
|
## Data format
|
|
|
The data are hdf5-files, the structure is based on the structure from the Berlin Brain Computer Interface Toolbox at https://github.com/bbci/bbci_public.
|
|
|
-Most fields have been removed, only some necessary fields are retained. We recommend to just use our loading code as above.
|
|
|
+Most fields have been removed, only some necessary fields are retained. We recommend to use our loading code as described above.
|
|
|
|
|
|
## Details of recording
|
|
|
|
