data/examples/paradigm_examples/plot_cross_subject_ssvep.py

"""
===========================
Cross-Subject SSVEP
===========================
This example shows how to perform a cross-subject analysis on an SSVEP dataset.
We will compare four pipelines :

- Riemannian Geometry
- CCA
- TRCA
- MsetCCA

We will use the SSVEP paradigm, which uses the AUC as metric.
"""

# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
#
# License: BSD (3-clause)

import warnings

import matplotlib.pyplot as plt
import pandas as pd
from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

import moabb
import moabb.analysis.plotting as moabb_plt
from moabb.analysis.chance_level import chance_by_chance
from moabb.datasets import Kalunga2016
from moabb.evaluations import CrossSubjectEvaluation
from moabb.paradigms import SSVEP, FilterBankSSVEP
from moabb.pipelines import SSVEP_CCA, SSVEP_TRCA, ExtendedSSVEPSignal, SSVEP_MsetCCA


warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=RuntimeWarning)
moabb.set_log_level("info")

###############################################################################
# Loading Dataset
# ---------------
#
# We will load the data from all 12 subjects of the ``SSVEP_Exo`` dataset
# and compare four algorithms on this set. One of the algorithms could only
# process class associated with a stimulation frequency, we will thus drop
# the resting class. As the resting class is the last defined class, picking
# the first three classes (out of four) allows to focus only on the stimulation
# frequency.

dataset = Kalunga2016()
interval = dataset.interval

###############################################################################
# Choose Paradigm
# ---------------
#
# We define the paradigms (SSVEP, SSVEP TRCA, SSVEP MsetCCA, and FilterBankSSVEP) and
# use the dataset Kalunga2016. All 3 SSVEP paradigms applied a bandpass filter (10-42 Hz) on
# the data, which include all stimuli frequencies and their first harmonics,
# while the FilterBankSSVEP paradigm uses as many bandpass filters as
# there are stimulation frequencies (here 3). For each stimulation frequency
# the EEG is filtered with a 1 Hz-wide bandpass filter centered on the
# frequency. This results in ``n_classes`` copies of the signal, filtered for each
# class, as used in the filterbank motor imagery paradigms.

paradigm = SSVEP(fmin=10, fmax=42, n_classes=3)
paradigm_TRCA = SSVEP(fmin=10, fmax=42, n_classes=3)
paradigm_MSET_CCA = SSVEP(fmin=10, fmax=42, n_classes=3)
paradigm_fb = FilterBankSSVEP(filters=None, n_classes=3)

###############################################################################
# Classes are defined by the frequency of the stimulation, here we use
# the first three frequencies of the dataset, 13, 17, and 21 Hz.
# The evaluation function uses a LabelEncoder, transforming them
# to 0, 1, and 2.

freqs = paradigm.used_events(dataset)

##############################################################################
# Create Pipelines
# ----------------
#
# Pipelines must be a dict of sklearn pipeline transformer.
# The first pipeline uses Riemannian geometry, by building an extended
# covariance matrices from the signal filtered around the considered
# frequency and applying a logistic regression in the tangent plane.
# The second pipeline relies on the above defined CCA classifier.
# The third pipeline relies on the TRCA algorithm,
# and the fourth uses the MsetCCA algorithm. Both CCA based methods
# (i.e. CCA and MsetCCA) used 3 CCA components.

pipelines_fb = {}
pipelines_fb["RG+LogReg"] = make_pipeline(
    ExtendedSSVEPSignal(),
    Covariances(estimator="lwf"),
    TangentSpace(),
    LogisticRegression(solver="lbfgs"),
)

pipelines = {}
pipelines["CCA"] = make_pipeline(SSVEP_CCA(n_harmonics=2))

pipelines_TRCA = {}
pipelines_TRCA["TRCA"] = make_pipeline(SSVEP_TRCA(n_fbands=3))

pipelines_MSET_CCA = {}
pipelines_MSET_CCA["MSET_CCA"] = make_pipeline(SSVEP_MsetCCA())

##############################################################################
# Evaluation
# ----------
#
# The evaluation will return a DataFrame containing an accuracy score for
# each subject / session of the dataset, and for each pipeline.
#
# Results are saved into the database, so that if you add a new pipeline, it
# will not run again the evaluation unless a parameter has changed. Results can
# be overwritten if necessary.

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

evaluation = CrossSubjectEvaluation(
    paradigm=paradigm, datasets=dataset, overwrite=overwrite
)
results = evaluation.process(pipelines)

###############################################################################
# Filter bank processing, determine the filter automatically from the
# stimulation frequency values of events.

evaluation_fb = CrossSubjectEvaluation(
    paradigm=paradigm_fb, datasets=dataset, overwrite=overwrite
)
results_fb = evaluation_fb.process(pipelines_fb)

###############################################################################
# TRCA processing also relies on filter bank that is automatically designed.

evaluation_TRCA = CrossSubjectEvaluation(
    paradigm=paradigm_TRCA, datasets=dataset, overwrite=overwrite
)
results_TRCA = evaluation_TRCA.process(pipelines_TRCA)

###############################################################################
# MsetCCA processing
evaluation_MSET_CCA = CrossSubjectEvaluation(
    paradigm=paradigm_MSET_CCA, datasets=dataset, overwrite=overwrite
)
results_MSET_CCA = evaluation_MSET_CCA.process(pipelines_MSET_CCA)

###############################################################################
# After processing the four, we simply concatenate the results.

results = pd.concat([results, results_fb, results_TRCA, results_MSET_CCA])

##############################################################################
# Plot Results
# ----------------
#
# Here we display the results using the MOABB score plot with chance level
# annotations. The 3-class SSVEP paradigm has a theoretical chance level
# of 33.3%.

chance_levels = chance_by_chance(results, alpha=[0.05, 0.01])

fig, _ = moabb_plt.score_plot(results, chance_level=chance_levels)
plt.show()