Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
* Fixing data meta info * Fixing description Cho and BNCI * Fixing order * Fixing the SSVEP and P300 * Adding new script * Fixing saving * Updating script * Fixing columns * Updating the script to process trial/events * Updating the whats_new.rst * Update docs/source/dataset_summary.rst Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update docs/source/dataset_summary.rst Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update docs/source/dataset_summary.rst Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update moabb/datasets/gigadb.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Update scripts/generating_metainfo.py Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr> * Fixing small things --------- Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>
- Loading branch information
1 parent
f153cf4
commit 2938fcc
Showing
8 changed files
with
230 additions
and
34 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,194 @@ | ||
| from argparse import ArgumentParser | ||
| from pathlib import Path | ||
|
|
||
| import mne | ||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| import moabb | ||
| from moabb.datasets.utils import dataset_search | ||
| from moabb.utils import set_download_dir | ||
|
|
||
|
|
||
| columns_name = [ | ||
| "Dataset", | ||
| "#Subj", | ||
| "#Chan", | ||
| "#Classes", | ||
| "trials/events", | ||
| "Window Size (s)", | ||
| "Freq (Hz)", | ||
| "#Session", | ||
| "#Runs", | ||
| "Total_trials", | ||
| ] | ||
|
|
||
|
|
||
| def parser_init(): | ||
| parser = ArgumentParser(description="Getting the meta-information script for MOABB") | ||
|
|
||
| parser.add_argument( | ||
| "-mne_p", | ||
| "--mne_data", | ||
| dest="mne_data", | ||
| default=Path.home() / "mne_data", | ||
| type=Path, | ||
| help="Folder where to save and load the datasets with mne structure.", | ||
| ) | ||
|
|
||
| return parser | ||
|
|
||
|
|
||
| def process_trial_freq(trials_per_events, prdgm): | ||
| """ | ||
| Function to process the trial frequency. | ||
| Getting the median value if the paradigm is MotorImagery. | ||
| Parameters | ||
| ---------- | ||
| trials_per_events: dict | ||
| prdgm: str | ||
| Returns | ||
| ------- | ||
| trial_freq: str | ||
| """ | ||
| class_per_trial = list(trials_per_events.values()) | ||
|
|
||
| if prdgm == "imagery" or prdgm == "ssvep": | ||
| return f"{int(np.median(class_per_trial))}" | ||
| elif prdgm == "p300": | ||
| not_target = max(trials_per_events.values()) | ||
| target = min(trials_per_events.values()) | ||
| return f"NT{not_target} / T {target}" | ||
|
|
||
|
|
||
| def get_meta_info(dataset, dataset_name, paradigm, prdgm_name): | ||
| """ | ||
| Function to get the meta-information of a dataset. | ||
| Parameters | ||
| ---------- | ||
| dataset: BaseDataset | ||
| Dataset object | ||
| dataset_name: str | ||
| Dataset name | ||
| paradigm: BaseParadigm | ||
| Paradigm object to process the dataset | ||
| prdgm_name: str | ||
| Paradigm name | ||
| Returns | ||
| ------- | ||
| """ | ||
| subjects = len(dataset.subject_list) | ||
| session = dataset.n_sessions | ||
|
|
||
| X, _, metadata = paradigm.get_data(dataset, [1], return_epochs=True) | ||
|
|
||
| sfreq = int(X.info["sfreq"]) | ||
| nchan = X.info["nchan"] | ||
| runs = len(metadata["run"].unique()) | ||
| classes = len(X.event_id) | ||
| epoch_size = X.tmax - X.tmin | ||
|
|
||
| trials_per_events = mne.count_events(X.events) | ||
| total_trials = int(sum(trials_per_events.values())) | ||
| trial_class = process_trial_freq(trials_per_events, prdgm_name) | ||
|
|
||
| info_dataset = pd.Series( | ||
| [ | ||
| dataset_name, | ||
| subjects, | ||
| nchan, | ||
| classes, | ||
| trial_class, | ||
| epoch_size, | ||
| sfreq, | ||
| session, | ||
| runs, | ||
| session * runs * total_trials * subjects, | ||
| ], | ||
| index=columns_name, | ||
| ) | ||
|
|
||
| return info_dataset | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| mne.set_log_level(False) | ||
|
|
||
| parser = parser_init() | ||
| options = parser.parse_args() | ||
| mne_path = Path(options.mne_data) | ||
|
|
||
| set_download_dir(mne_path) | ||
|
|
||
| paradigms = {} | ||
| paradigms["imagery"] = moabb.paradigms.MotorImagery() | ||
| paradigms["ssvep"] = moabb.paradigms.SSVEP() | ||
| paradigms["p300"] = moabb.paradigms.P300() | ||
|
|
||
| for prdgm_name, paradigm in paradigms.items(): | ||
| dataset_list = dataset_search(paradigm=prdgm_name) | ||
|
|
||
| metainfo = [] | ||
| for dataset in dataset_list: | ||
| dataset_name = str(dataset).split(".")[-1].split(" ")[0] | ||
|
|
||
| dataset_path = f"{mne_path.parent}/metainfo/metainfo_{dataset_name}.csv" | ||
|
|
||
| if not dataset_path.exists(): | ||
| print( | ||
| "Trying to get the meta information from the " | ||
| f"dataset {dataset} with {prdgm_name}" | ||
| ) | ||
|
|
||
| try: | ||
| info_dataset = get_meta_info( | ||
| dataset, dataset_name, paradigm, prdgm_name | ||
| ) | ||
| print( | ||
| "Saving the meta information for the dataset in the file: ", | ||
| dataset_path, | ||
| ) | ||
| info_dataset.to_csv(dataset_path) | ||
| metainfo.append(info_dataset) | ||
|
|
||
| except Exception as ex: | ||
| print(f"Error with {dataset} with {prdgm_name} paradigm", end=" ") | ||
| print(f"Error: {ex}") | ||
|
|
||
| if prdgm_name == "imagery": | ||
| print("Trying with the LeftRightImagery paradigm") | ||
| prdgm2 = moabb.paradigms.LeftRightImagery() | ||
| try: | ||
| info_dataset = get_meta_info( | ||
| dataset, dataset_name, prdgm2, prdgm_name | ||
| ) | ||
| print( | ||
| "Saving the meta information for the dataset in the file: ", | ||
| dataset_path, | ||
| ) | ||
| info_dataset.to_csv(dataset_path) | ||
| metainfo.append(info_dataset) | ||
|
|
||
| except Exception as ex: | ||
| print( | ||
| f"Error with {dataset} with {prdgm_name} paradigm", | ||
| end=" ", | ||
| ) | ||
| print(f"Error: {ex}") | ||
| else: | ||
| print(f"Loading the meta information from {dataset_path}") | ||
| info_dataset = pd.read_csv(dataset_path) | ||
| metainfo.append(info_dataset) | ||
|
|
||
| paradigm_df = pd.concat(metainfo, axis=1).T | ||
|
|
||
| paradigm_df.columns = columns_name | ||
| dataset_path = mne_path.parent / "metainfo" / f"metainfo_{dataset_name}.csv" | ||
| print(f"Saving the meta information for the paradigm {dataset_path}") | ||
|
|
||
| paradigm_df.to_csv(dataset_path, index=None) |