plot_cross_session_ssvep.py

"""
===================
Cross Session SSVEP
===================

This Example show how to perform a cross-session SSVEP analysis on the
MAMEM dataset 3, using a CCA pipeline.

The cross session evaluation context will evaluate performance using a leave
one session out cross-validation. For each session in the dataset, a model
is trained on every other session and performance are evaluated on the current
session.
"""
# 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 MAMEM3
from moabb.evaluations import CrossSessionEvaluation
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 MAMEM3 dataset, with 3 session each

subj = [1, 3]
dataset = MAMEM3()
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 6.66, 7.50 and 8.57 Hz.

paradigm = SSVEP(fmin=3, fmax=15, 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(interval=interval, freqs=freqs, 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 / session of the dataset, and for each pipeline.

overwrite = True  # set to True if we want to overwrite cached results

evaluation = CrossSessionEvaluation(
    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 session and subject

plt.figure()
sns.barplot(data=results, y="score", x="session", hue="subject", palette="viridis")

plt.show()