| """ | |
| ===================== | |
| Within Session SSVEP | |
| ===================== | |
| This Example shows how to perform a within-session SSVEP analysis on the | |
| MAMEM dataset 3, using a CCA pipeline. | |
| The within-session evaluation assesses the performance of a classification | |
| pipeline using a 5-fold cross-validation. The reported metric (here, accuracy) | |
| is the average of all fold. | |
| """ | |
| # Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr> | |
| # | |
| # License: BSD (3-clause) | |
| import warnings | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from sklearn.pipeline import make_pipeline | |
| import moabb | |
| from moabb.datasets import Kalunga2016 | |
| from moabb.evaluations import WithinSessionEvaluation | |
| from moabb.paradigms import SSVEP | |
| from moabb.pipelines import SSVEP_CCA | |
| warnings.simplefilter(action="ignore", category=FutureWarning) | |
| warnings.simplefilter(action="ignore", category=RuntimeWarning) | |
| moabb.set_log_level("info") | |
| ############################################################################### | |
| # Loading Dataset | |
| # --------------- | |
| # | |
| # Load 2 subjects of Kalunga2016 dataset | |
| subj = [1, 3] | |
| dataset = Kalunga2016() | |
| dataset.subject_list = subj | |
| ############################################################################### | |
| # Choose Paradigm | |
| # --------------- | |
| # | |
| # We select the paradigm SSVEP, applying a bandpass filter (3-15 Hz) on | |
| # the data and we keep only the first 3 classes, that is stimulation | |
| # frequency of 13Hz, 17Hz and 21Hz. | |
| paradigm = SSVEP(fmin=10, fmax=40, n_classes=3) | |
| ############################################################################## | |
| # Create Pipelines | |
| # ---------------- | |
| # | |
| # Use a Canonical Correlation Analysis classifier | |
| interval = dataset.interval | |
| freqs = paradigm.used_events(dataset) | |
| pipeline = {} | |
| pipeline["CCA"] = make_pipeline(SSVEP_CCA(n_harmonics=3)) | |
| ############################################################################## | |
| # Get Data (optional) | |
| # ------------------- | |
| # | |
| # To get access to the EEG signals downloaded from the dataset, you could | |
| # use `dataset.get_data(subjects=[subject_id])` to obtain the EEG under | |
| # MNE format, stored in a dictionary of sessions and runs. | |
| # Otherwise, `paradigm.get_data(dataset=dataset, subjects=[subject_id])` | |
| # allows to obtain the EEG data in scikit format, the labels and the meta | |
| # information. In `paradigm.get_data`, the EEG are preprocessed according | |
| # to the paradigm requirement. | |
| # sessions = dataset.get_data(subjects=[3]) | |
| # X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[3]) | |
| ############################################################################## | |
| # Evaluation | |
| # ---------- | |
| # | |
| # The evaluation will return a DataFrame containing a single AUC score for | |
| # each subject and pipeline. | |
| overwrite = True # set to True if we want to overwrite cached results | |
| evaluation = WithinSessionEvaluation( | |
| paradigm=paradigm, datasets=dataset, suffix="examples", overwrite=overwrite | |
| ) | |
| results = evaluation.process(pipeline) | |
| print(results.head()) | |
| ############################################################################## | |
| # Plot Results | |
| # ---------------- | |
| # | |
| # Here we plot the results, indicating the score for each subject | |
| plt.figure() | |
| sns.barplot(data=results, y="score", x="session", hue="subject", palette="viridis") | |
| ############################################################################## | |
| # And the computation time in seconds | |
| plt.figure() | |
| ax = sns.barplot(data=results, y="time", x="session", hue="subject", palette="Reds") | |
| ax.set_ylabel("Time (s)") | |
| plt.show() | |