##########################################################################
# If you would like to specify the logging level when it is running, you can
# use the standard python logging commands through the top-level moabb module
import moabb
from moabb.datasets import BNCI2014001, utils
from moabb.evaluations import CrossSessionEvaluation
from moabb.paradigms import LeftRightImagery
from moabb.pipelines.features import LogVariance
##########################################################################
# In order to create pipelines within a script, you will likely need at least
# the make_pipeline function. They can also be specified via a .yml file. Here
# we will make a couple pipelines just for convenience
moabb.set_log_level("info")
##############################################################################
# Create pipelines
# ----------------
#
# We create two pipelines: channel-wise log variance followed by LDA, and
# channel-wise log variance followed by a cross-validated SVM (note that a
# cross-validation via scikit-learn cannot be described in a .yml file). For
# later in the process, the pipelines need to be in a dictionary where the key
# is the name of the pipeline and the value is the Pipeline object
pipelines = {}
pipelines["AM+LDA"] = make_pipeline(LogVariance(), LDA())
parameters = {"C": np.logspace(-2, 2, 10)}
clf = GridSearchCV(SVC(kernel="linear"), parameters)
import seaborn as sns
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from mne.decoding import CSP
from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace
from moabb.datasets import BNCI2014001c
from moabb.paradigms import LeftRightImagery
from moabb.evaluations import CrossSessionEvaluation
moabb.set_log_level('info')
##############################################################################
# Create pipelines
# ----------------
#
# Pipelines must be a dict of sklearn pipeline transformer.
#
# The csp implementation from MNE is used. We selected 8 CSP components, as
# usually done in the litterature.
#
# The riemannian geometry pipeline consists in covariance estimation, tangent
# space mapping and finaly a logistic regression for the classification.
pipelines = {}
from braindecode.models import * #ShallowFBCSPNet, EEGNetv1
from skorch.callbacks import LRScheduler
from skorch.helper import predefined_split
from braindecode import EEGClassifier, EEGClassifier_weighted
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import pandas as pd
import mne
mne.set_log_level(False)
import moabb
moabb.set_log_level(False)
# import sys
# sys.path.insert(0, '/run/media/george/purple_data/codes/git_repos/skorch')
# import skorch
######################################################################
def train(subject_id):
print('\n--------------------------------------------------\n')
print(
'Training on BCI_IV_2a dataset | Cross-subject | ID: {:02d}\n'.format(
subject_id))
sessions = None
else:
raise ValueError(
'Currently, mixed subject:session and only subject syntax is not supported.'
)
print(f'Subjects: {subjects}')
print(f'Sessions: {sessions}')
start_timestamp_as_str = dt.now().replace(microsecond=0).isoformat().replace(
":", "-")
warnings.simplefilter(action='ignore', category=RuntimeWarning)
warnings.simplefilter(action='ignore', category=UserWarning)
warnings.simplefilter(action='ignore', category=FutureWarning)
moabb.set_log_level('warn')
np.random.seed(42)
##############################################################################
# Create pipelines
##############################################################################
labels_dict = {'Target': 1, 'NonTarget': 0}
prepro_cfg = ana_cfg['default']['data_preprocessing']
bench_cfg = get_benchmark_config(dataset_name,
prepro_cfg,
subjects=subjects,
sessions=sessions)
