Classification of P300 datasets from MOABB using NCH

Demonstrates classification with QuantumNCH. Evaluation is done using MOABB.

If parameter “shots” is None then a classical SVM is used similar to the one in scikit learn. If “shots” is not None and IBM Qunatum token is provided with “q_account_token” then a real Quantum computer will be used. You also need to adjust the “n_components” in the PCA procedure to the number of qubits supported by the real quantum computer you are going to use. A list of real quantum computers is available in your IBM quantum account.

# Author: Anton Andreev
# Modified from plot_classify_EEG_tangentspace.py of pyRiemann
# License: BSD (3-clause)

# import warnings

import seaborn as sns
from matplotlib import pyplot as plt
from moabb import set_log_level
from moabb.datasets import bi2013a
from moabb.evaluations import WithinSessionEvaluation
from moabb.paradigms import P300
from pyriemann.classification import MDM
from pyriemann.estimation import XdawnCovariances
from sklearn.pipeline import make_pipeline

from pyriemann_qiskit.classification import QuanticNCH

print(__doc__)

getting rid of the warnings about the future warnings.simplefilter(action=”ignore”, category=FutureWarning) warnings.simplefilter(action=”ignore”, category=RuntimeWarning)

# warnings.filterwarnings("ignore")

set_log_level("info")

Create Pipelines

Pipelines must be a dict of sklearn pipeline transformer.

We have to do this because the classes are called ‘Target’ and ‘NonTarget’ but the evaluation function uses a LabelEncoder, transforming them to 0 and 1

labels_dict = {"Target": 1, "NonTarget": 0}

paradigm = P300(resample=128)

datasets = [bi2013a()]  # MOABB provides several other P300 datasets

# reduce the number of subjects, the Quantum pipeline takes a lot of time
# if executed on the entire dataset
n_subjects = 1
for dataset in datasets:
    dataset.subject_list = dataset.subject_list[0:n_subjects]

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

pipelines = {}

pipelines["NCH+RANDOM_HULL"] = make_pipeline(
    # applies XDawn and calculates the covariance matrix, output it matrices
    XdawnCovariances(
        nfilter=3,
        classes=[labels_dict["Target"]],
        estimator="lwf",
        xdawn_estimator="scm",
    ),
    QuanticNCH(
        n_hulls_per_class=1,
        n_samples_per_hull=3,
        n_jobs=12,
        subsampling="random",
        quantum=False,
    ),
)

pipelines["NCH+MIN_HULL"] = make_pipeline(
    # applies XDawn and calculates the covariance matrix, output it matrices
    XdawnCovariances(
        nfilter=3,
        classes=[labels_dict["Target"]],
        estimator="lwf",
        xdawn_estimator="scm",
    ),
    QuanticNCH(
        n_hulls_per_class=1,
        n_samples_per_hull=3,
        n_jobs=12,
        subsampling="min",
        quantum=False,
    ),
)

# this is a non quantum pipeline
pipelines["XD+MDM"] = make_pipeline(
    XdawnCovariances(
        nfilter=3,
        classes=[labels_dict["Target"]],
        estimator="lwf",
        xdawn_estimator="scm",
    ),
    MDM(),
)

print("Total pipelines to evaluate: ", len(pipelines))

evaluation = WithinSessionEvaluation(
    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
)

results = evaluation.process(pipelines)

print("Averaging the session performance:")
print(results.groupby("pipeline").mean("score")[["score", "time"]])

Plot Results

Here we plot the results to compare the two pipelines

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("ROC AUC")
ax.set_ylim(0.3, 1)

plt.show()