moabb/examples/plot_cross_subject_ssvep.py at develop · goodcpp/moabb · GitHub

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"""

===========================

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

import seaborn as sns

from pyriemann.estimation import Covariances

from pyriemann.tangentspace import TangentSpace

from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import make_pipeline

import moabb

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 the first 2 subjects of the ``SSVEP_Exo`` dataset

# and compare two 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.

n_subject = 2

dataset = Kalunga2016()

dataset.subject_list = dataset.subject_list[:n_subject]

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 two frequencies of the dataset, 13 and 17 Hz.

# The evaluation function uses a LabelEncoder, transforming them

# to 0 and 1

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", multi_class="auto"),

)

pipelines = {}

pipelines["CCA"] = make_pipeline(SSVEP_CCA(interval=interval, freqs=freqs, n_harmonics=2))

pipelines_TRCA = {}

pipelines_TRCA["TRCA"] = make_pipeline(SSVEP_TRCA(interval=interval, freqs=freqs))

pipelines_MSET_CCA = {}

pipelines_MSET_CCA["MSET_CCA"] = make_pipeline(SSVEP_MsetCCA(freqs=freqs))

##############################################################################

# 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 = False # 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 as stripplot, with a pointplot for error bar.

fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])

sns.stripplot(

data=results,

y="score",

x="pipeline",

ax=ax,

jitter=True,

alpha=0.5,

zorder=1,

palette="Set1",

)

sns.pointplot(data=results, y="score", x="pipeline", ax=ax, palette="Set1")

ax.set_ylabel("Accuracy")

ax.set_ylim(0.1, 0.6)

plt.show()

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