示例#1
0
ts_lr = make_pipeline(Covariances(estimator='oas'),
                      TangentSpace(metric='riemann'), LR(C=1.0))
results = evaluation.process({'csp+lda': csp_lda, 'ts+lr': ts_lr})
print(results.head())

##############################################################################
# Electrode selection
# -------------------
#
# It is possible to select the electrodes that are shared by all datasets
# using the `find_intersecting_channels` function. Datasets that have 0
# overlap with others are discarded. It returns the set of common channels,
# as well as the list of datasets with valid channels.

electrodes, datasets = find_intersecting_channels(datasets)
evaluation = WithinSessionEvaluation(paradigm=paradigm,
                                     datasets=datasets,
                                     overwrite=True)
results = evaluation.process({'csp+lda': csp_lda, 'ts+lr': ts_lr})
print(results.head())

##############################################################################
# Plot results
# ------------
#
# Compare the obtained results with the two pipelines, CSP+LDA and logistic
# regression computed in the tangent space of the covariance matrices.

fig = moabb_plt.paired_plot(results, 'csp+lda', 'ts+lr')
plt.show()
示例#2
0
# ----------------
#
# Here we plot the results using some of the convenience methods within the
# toolkit.  The score_plot visualizes all the data with one score per subject
# for every dataset and pipeline.

fig = moabb_plt.score_plot(results)
plt.show()

###############################################################################
# For a comparison of two algorithms, there is the paired_plot, which plots
# performance in one versus the performance in the other over all chosen
# datasets. Note that there is only one score per subject, regardless of the
# number of sessions.

fig = moabb_plt.paired_plot(results, "CSP+LDA", "RG+LDA")
plt.show()

###############################################################################
# Statistical testing and further plots
# ----------------------------------------
#
# If the statistical significance of results is of interest, the method
# compute_dataset_statistics allows one to show a meta-analysis style plot as
# well. For an overview of how all algorithms perform in comparison with each
# other, the method find_significant_differences and the summary_plot are
# possible.

stats = compute_dataset_statistics(results)
P, T = find_significant_differences(stats)
示例#3
0
    LogisticRegression(penalty="l1", solver="liblinear"),
)
sk_eval = CrossSessionEvaluation(
    paradigm=paradigm,
    datasets=datasets,
    suffix="examples",
    overwrite=True,
)
sk_res = sk_eval.process(sk_ppl)

###############################################################################
# Combining results
# -----------------
#
# Even if the results have been obtained by different evaluation processes, it
# possible to combine the resulting dataframes to analyze and plot the results.

all_res = pd.concat([mne_res, adv_res, sk_res])

# We could compare the Euclidean and Riemannian performance using a `paired_plot`

paired_plot(all_res, "XDAWN LR", "RG LR")

# All the results could be compared and statistical analysis could highlight the
# differences between pipelines.

stats = compute_dataset_statistics(all_res)
P, T = find_significant_differences(stats)
summary_plot(P, T)
plt.show()
示例#4
0
# ----------------
#
# Here we plot the results using some of the convenience methods within the
# toolkit.  The score_plot visualizes all the data with one score per subject
# for every dataset and pipeline.

fig = moabb_plt.score_plot(results)
plt.show()

###############################################################################
# For a comparison of two algorithms, there is the paired_plot, which plots
# performance in one versus the performance in the other over all chosen
# datasets. Note that there is only one score per subject, regardless of the
# number of sessions.

fig = moabb_plt.paired_plot(results, 'CSP + LDA', 'RG + LDA')
plt.show()

###############################################################################
# Statistical testing and further plots
# ----------------------------------------
#
# If the statistical significance of results is of interest, the method
# compute_dataset_statistics allows one to show a meta-analysis style plot as
# well. For an overview of how all algorithms perform in comparison with each
# other, the method find_significant_differences and the summary_plot are
# possible.

stats = compute_dataset_statistics(results)
P, T = find_significant_differences(stats)
示例#5
0
ts_lr = make_pipeline(Covariances(estimator="oas"),
                      TangentSpace(metric="riemann"), LR(C=1.0))
results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
print(results.head())

##############################################################################
# Electrode selection
# -------------------
#
# It is possible to select the electrodes that are shared by all datasets
# using the `find_intersecting_channels` function. Datasets that have 0
# overlap with others are discarded. It returns the set of common channels,
# as well as the list of datasets with valid channels.

electrodes, datasets = find_intersecting_channels(datasets)
evaluation = WithinSessionEvaluation(paradigm=paradigm,
                                     datasets=datasets,
                                     overwrite=True)
results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
print(results.head())

##############################################################################
# Plot results
# ------------
#
# Compare the obtained results with the two pipelines, CSP+LDA and logistic
# regression computed in the tangent space of the covariance matrices.

fig = moabb_plt.paired_plot(results, "csp+lda", "ts+lr")
plt.show()