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-# high-gamma-dataset
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+# High Gamma Dataset
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+
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+This is the documnentation for the High Gamma Dataset used in "Deep learning with convolutional neural networks for EEG decoding and visualization"
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+ (https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730).
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+See the paper and supporting information for a general description.
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+
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+## Download
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+Download the files from here:
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+https://www.dropbox.com/sh/vgxed0kx0b31aiv/AAAS2E7qXpn0vPjqjMLVW4ZOa?dl=0
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+
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+
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+## Usage of this dataset
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+
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+The braindecode toolbox at https://github.com/robintibor/braindecode provides code to load this dataset in python.
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+You can run the following code to get an MNE RawArray:
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+
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+```python
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+from braindecode.datasets.bbci import BBCIDataset
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+cnt = BBCIDataset(filename='./test/1.mat', load_sensor_names=None).load()
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+```
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+
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+The `example.py` code in this repository shows how to reproduce the decoding results for our dataset.
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+
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+
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+## Data format
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+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.
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+Most fields have been removed, only some necessary fields are retained. We recommend to just use our loading code as above.
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+
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+## Details of recording
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+
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+The recodings were referenced to Cz, however in our recording setup, some residual signal remains on Cz.
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+Note that for subject 14, about half of the sensors lost meaningful signal in the test set.
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+It is still possible to get far-above chance accuracies even when not accounting for this in any way when training on all sensors of the training set.
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+
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+## Citing
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+If you use this dataset in a scientific publication, please cite the above-mentioned HBM-paper as:
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+
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+```
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+ @article {HBM:HBM23730,
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+ author = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,
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+ Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and
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+ Hutter, Frank and Burgard, Wolfram and Ball, Tonio},
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+ title = {Deep learning with convolutional neural networks for EEG decoding and visualization},
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+ journal = {Human Brain Mapping},
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+ issn = {1097-0193},
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+ url = {http://dx.doi.org/10.1002/hbm.23730},
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+ doi = {10.1002/hbm.23730},
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+ month = {aug},
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+ year = {2017},
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+ keywords = {electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,
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+ brain–computer interface, model interpretability, brain mapping},
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+ }
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+```
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