Note
Click here to download the full example code
Hinss2021 classification example#
This example shows how to use the Hinss2021 dataset with the resting state paradigm.
In this example, we aim to determine the most effective channel selection strategy
for the moabb.datasets.Hinss2021
dataset.
The pipelines under consideration are:
Xdawn
Electrode selection based on time epochs data
Electrode selection based on covariance matrices
# License: BSD (3-clause)
import warnings
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from pyriemann.channelselection import ElectrodeSelection
from pyriemann.estimation import Covariances
from pyriemann.spatialfilters import Xdawn
from pyriemann.tangentspace import TangentSpace
from sklearn.base import TransformerMixin
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.pipeline import make_pipeline
from moabb import set_log_level
from moabb.datasets import Hinss2021
from moabb.evaluations import CrossSessionEvaluation
from moabb.paradigms import RestingStateToP300Adapter
# Suppressing future and runtime warnings for cleaner output
warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=RuntimeWarning)
set_log_level("info")
Create util transformer#
Let’s create a scikit transformer mixin, that will select electrodes based on the covariance information
class EpochSelectChannel(TransformerMixin):
"""Select channels based on covariance information."""
def __init__(self, n_chan, cov_est):
self._chs_idx = None
self.n_chan = n_chan
self.cov_est = cov_est
def fit(self, X, _y=None):
# Get the covariances of the channels for each epoch.
covs = Covariances(estimator=self.cov_est).fit_transform(X)
# Get the average covariance between the channels
m = np.mean(covs, axis=0)
n_feats, _ = m.shape
# Select the `n_chan` channels having the maximum covariances.
all_max = []
for i in range(n_feats):
for j in range(n_feats):
if len(all_max) <= self.n_chan:
all_max.append(m[i, j])
else:
if m[i, j] > max(all_max):
all_max[np.argmin(all_max)] = m[i, j]
indices = []
for v in all_max:
indices.extend(np.argwhere(m == v).flatten())
# We will keep only these channels for the transform step.
indices = np.unique(indices)
self._chs_idx = indices
return self
def transform(self, X):
return X[:, self._chs_idx, :]
Initialization Process#
Define the experimental paradigm object (RestingState)
Load the datasets
Select a subset of subjects and specific events for analysis
# Here we define the mne events for the RestingState paradigm.
events = dict(easy=2, diff=3)
# The paradigm is adapted to the P300 paradigm.
paradigm = RestingStateToP300Adapter(events=events, tmin=0, tmax=0.5)
# We define a list with the dataset to use
datasets = [Hinss2021()]
# To reduce the computation time in the example, we will only use the
# first two subjects.
n__subjects = 2
title = "Datasets: "
for dataset in datasets:
title = title + " " + dataset.code
dataset.subject_list = dataset.subject_list[:n__subjects]
Create Pipelines#
Pipelines must be a dict of scikit-learning pipeline transformer.
pipelines = {}
pipelines["Xdawn+Cov+TS+LDA"] = make_pipeline(
Xdawn(nfilter=4), Covariances(estimator="lwf"), TangentSpace(), LDA()
)
pipelines["Cov+ElSel+TS+LDA"] = make_pipeline(
Covariances(estimator="lwf"), ElectrodeSelection(nelec=8), TangentSpace(), LDA()
)
# Pay attention here that the channel selection took place before computing the covariances:
# It is done on time epochs.
pipelines["ElSel+Cov+TS+LDA"] = make_pipeline(
EpochSelectChannel(n_chan=8, cov_est="lwf"),
Covariances(estimator="lwf"),
TangentSpace(),
LDA(),
)
Run evaluation#
Compare the pipeline using a cross session evaluation.
# Here should be cross-session
evaluation = CrossSessionEvaluation(
paradigm=paradigm,
datasets=datasets,
overwrite=False,
)
results = evaluation.process(pipelines)
2024-10-24 08:51:29,775 INFO MainThread moabb.evaluations.base Processing dataset: Hinss2021
Hinss2021-CrossSession: 0%| | 0/2 [00:00<?, ?it/s]MNE_DATA is not already configured. It will be set to default location in the home directory - /home/runner/mne_data
All datasets will be downloaded to this location, if anything is already downloaded, please move manually to this location
/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1064: InsecureRequestWarning: Unverified HTTPS request is being made to host 'zenodo.org'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
warnings.warn(
/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1064: InsecureRequestWarning: Unverified HTTPS request is being made to host 'zenodo.org'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
warnings.warn(
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/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 298 events (all good), 0 – 0.5 s (baseline off), ~34.9 MB, data loaded,
'easy': 149
'diff': 149>
warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 298 events (all good), 0 – 0.5 s (baseline off), ~34.9 MB, data loaded,
'easy': 149
'diff': 149>
warn(f"warnEpochs {epochs}")
2024-10-24 08:52:43,230 INFO MainThread moabb.evaluations.base Xdawn+Cov+TS+LDA | Hinss2021 | 1 | 1: Score 0.712
2024-10-24 08:52:43,756 INFO MainThread moabb.evaluations.base Xdawn+Cov+TS+LDA | Hinss2021 | 1 | 2: Score 0.473
2024-10-24 08:52:45,642 INFO MainThread moabb.evaluations.base Cov+ElSel+TS+LDA | Hinss2021 | 1 | 1: Score 0.597
2024-10-24 08:52:47,522 INFO MainThread moabb.evaluations.base Cov+ElSel+TS+LDA | Hinss2021 | 1 | 2: Score 0.791
2024-10-24 08:52:48,114 INFO MainThread moabb.evaluations.base ElSel+Cov+TS+LDA | Hinss2021 | 1 | 1: Score 0.885
2024-10-24 08:52:48,716 INFO MainThread moabb.evaluations.base ElSel+Cov+TS+LDA | Hinss2021 | 1 | 2: Score 0.687
Hinss2021-CrossSession: 50%|##### | 1/2 [01:19<01:19, 79.11s/it]/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1064: InsecureRequestWarning: Unverified HTTPS request is being made to host 'zenodo.org'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
warnings.warn(
/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1064: InsecureRequestWarning: Unverified HTTPS request is being made to host 'zenodo.org'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
warnings.warn(
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/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 298 events (all good), 0 – 0.5 s (baseline off), ~34.9 MB, data loaded,
'easy': 149
'diff': 149>
warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 298 events (all good), 0 – 0.5 s (baseline off), ~34.9 MB, data loaded,
'easy': 149
'diff': 149>
warn(f"warnEpochs {epochs}")
2024-10-24 08:55:00,889 INFO MainThread moabb.evaluations.base Xdawn+Cov+TS+LDA | Hinss2021 | 2 | 1: Score 0.712
2024-10-24 08:55:01,427 INFO MainThread moabb.evaluations.base Xdawn+Cov+TS+LDA | Hinss2021 | 2 | 2: Score 0.738
2024-10-24 08:55:03,462 INFO MainThread moabb.evaluations.base Cov+ElSel+TS+LDA | Hinss2021 | 2 | 1: Score 0.773
2024-10-24 08:55:05,387 INFO MainThread moabb.evaluations.base Cov+ElSel+TS+LDA | Hinss2021 | 2 | 2: Score 0.920
2024-10-24 08:55:05,986 INFO MainThread moabb.evaluations.base ElSel+Cov+TS+LDA | Hinss2021 | 2 | 1: Score 0.783
2024-10-24 08:55:06,587 INFO MainThread moabb.evaluations.base ElSel+Cov+TS+LDA | Hinss2021 | 2 | 2: Score 0.591
Hinss2021-CrossSession: 100%|##########| 2/2 [03:36<00:00, 113.67s/it]
Hinss2021-CrossSession: 100%|##########| 2/2 [03:36<00:00, 108.49s/it]
Here, with the ElSel+Cov+TS+LDA pipeline, we reduce the computation time in approximately 8 times to the Cov+ElSel+TS+LDA pipeline.
print("Averaging the session performance:")
print(results.groupby("pipeline").mean("score")[["score", "time"]])
Averaging the session performance:
score time
pipeline
Cov+ElSel+TS+LDA 0.770303 1.758169
ElSel+Cov+TS+LDA 0.736622 0.422598
Xdawn+Cov+TS+LDA 0.658810 0.378571
Plot Results#
Here, we plot the results to compare 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").set(
title=title
)
ax.set_ylabel("ROC AUC")
ax.set_ylim(0.3, 1)
plt.show()
Key Observations:#
Xdawn is not ideal for the resting state paradigm. This is due to its specific design for Event-Related Potential (ERP).
Electrode selection strategy based on covariance matrices demonstrates less variability and typically yields better performance.
However, this strategy is more time-consuming compared to the simpler electrode selection based on time epoch data.
Total running time of the script: ( 3 minutes 38.625 seconds)
Estimated memory usage: 580 MB