# ----------------- # # Datasets can be specified in many ways: Each paradigm has a property # 'datasets' which returns the datasets that are appropriate for that paradigm print(LeftRightImagery().datasets) ########################################################################## # Or you can run a search through the available datasets: print(utils.dataset_search(paradigm="imagery", min_subjects=6)) ########################################################################## # Or you can simply make your own list (which we do here due to computational # constraints) dataset = BNCI2014001() dataset.subject_list = dataset.subject_list[:2] datasets = [dataset] ########################################################################## # Paradigm # -------------------- # # Paradigms define the events, epoch time, bandpass, and other preprocessing # parameters. They have defaults that you can read in the documentation, or you # can simply set them as we do here. A single paradigm defines a method for # going from continuous data to trial data of a fixed size. To learn more look # at the tutorial Exploring Paradigms fmin = 8 fmax = 35
from sklearn.linear_model import LogisticRegression as LR from mne.decoding import CSP from pyriemann.estimation import Covariances from pyriemann.tangentspace import TangentSpace import matplotlib.pyplot as plt import moabb.analysis.plotting as moabb_plt ############################################################################## # Datasets # -------- # # Select datasets for motor imagery datasets = [Zhou2016(), BNCI2014001()] ############################################################################## # Paradigm # -------- # # Restrict further analysis to specified channels, here C3, C4, and Cz. # Also, use a specific resampling. In this example, all datasets are # set to 200 Hz. paradigm = LeftRightImagery(channels=['C3', 'C4', 'Cz'], resample=200.) ############################################################################## # Evaluation # ---------- #
# We instantiate the three different classiciation pipelines to be considered
# in the analysis. The object that gathers each pipeline is a dictionary. The
# first pipeline is the CSP+LDA that we have seen in the previous parts. The
# other two pipelines rely on Riemannian geometry, using an SVM classification
# in the tangent space of the covariance matrices estimated from the EEG or a
# MDM classifier that works directly on covariance matrices.
pipelines = {}
pipelines["csp+lda"] = make_pipeline(CSP(n_components=8), LDA())
pipelines["tgsp+svm"] = make_pipeline(Covariances('oas'),
TangentSpace(metric='riemann'),
SVC(kernel='linear'))
pipelines["MDM"] = make_pipeline(Covariances('oas'), MDM(metric='riemann'))
# The following lines go exactly as in the previous example, where we end up
# obtaining a pandas dataframe containing the results of the evaluation.
datasets = [BNCI2014001(), Weibo2014(), Zhou2016()]
paradigm = LeftRightImagery()
evaluation = WithinSessionEvaluation(paradigm=paradigm,
datasets=datasets,
overwrite=True)
results = evaluation.process(pipelines)
if not os.path.exists("./results"):
os.mkdir("./results")
results.to_csv("./results/results_part2-3.csv")
results = pd.read_csv('./results/results_part2-3.csv')
##############################################################################
# Plotting Results
# ----------------
#
# The following plot shows a comparison of the three classification pipelines
# ----------------- # # Datasets can be specified in many ways: Each paradigm has a property # 'datasets' which returns the datasets that are appropriate for that paradigm print(LeftRightImagery().datasets) ########################################################################## # Or you can run a search through the available datasets: print(utils.dataset_search(paradigm="imagery", min_subjects=6)) ########################################################################## # Or you can simply make your own list (which we do here due to computational # constraints) datasets = [BNCI2014001()] ########################################################################## # Paradigm # -------------------- # # Paradigms define the events, epoch time, bandpass, and other preprocessing # parameters. They have defaults that you can read in the documentation, or you # can simply set them as we do here. A single paradigm defines a method for # going from continuous data to trial data of a fixed size. To learn more look # at the tutorial Exploring Paradigms fmin = 8 fmax = 35 paradigm = LeftRightImagery(fmin=fmin, fmax=fmax)
# first pipeline is the CSP+LDA that we have seen in the previous parts. The
# other two pipelines rely on Riemannian geometry, using an SVM classification
# in the tangent space of the covariance matrices estimated from the EEG or a
# MDM classifier that works directly on covariance matrices.
pipelines = {}
pipelines["csp+lda"] = make_pipeline(CSP(n_components=8), LDA())
pipelines["tgsp+svm"] = make_pipeline(Covariances("oas"),
TangentSpace(metric="riemann"),
SVC(kernel="linear"))
pipelines["MDM"] = make_pipeline(Covariances("oas"), MDM(metric="riemann"))
##############################################################################
# The following lines go exactly as in the previous tutorial, where we end up
# obtaining a pandas dataframe containing the results of the evaluation.
datasets = [BNCI2014001(), Zhou2016()]
subj = [1, 2, 3]
for d in datasets:
d.subject_list = subj
paradigm = LeftRightImagery()
evaluation = WithinSessionEvaluation(paradigm=paradigm,
datasets=datasets,
overwrite=False)
results = evaluation.process(pipelines)
##############################################################################
# As `overwrite` is set to False, the results from the previous tutorial are reused and
# only the new pipelines are evaluated. The results from "csp+lda" are not recomputed.
# The results are saved in ~/mne_data/results if the parameter `hdf5_path` is not set.
##############################################################################
