filepaths = [
Path(
f"/home/paulo/Documents/datasets/BCI_Comp_IV_2a/gdf/{uid}E.gdf"
),
# Path(f"/home/paulo/Documents/datasets/BCI_Comp_IV_2a/gdf/{uid}E.gdf"),
]
# BCI_IV_Comp_Dataset.EVENT_MAP_DICT = {
# "276": 0,
# "277": 1,
# "1072": 2
# }
BCI_IV_Comp_Dataset.EVENT_MAP_DICT = {"277": 0, "276": 1, "1072": 2}
epochs_list = [
BCI_IV_Comp_Dataset.load_as_epochs(filepaths[0],
load_eog=True,
tmin=4.,
tmax=60.,
reject=False).load_data(),
# BCI_IV_Comp_Dataset.load_as_epochs(filepaths[1], load_eog=True, tmin=4., tmax=60., reject=False, has_labels=False).load_data(),
]
epochs = concatenate_epochs(epochs_list)
# eeg_epochs = epochs.copy().pick("eeg").pick(['EEG-Fz', 'EEG-C3', 'EEG-Cz', 'EEG-C4', 'EEG-Pz'])
# eog_epochs = epochs.copy().pick("eog").pick(['EOG-left', 'EOG-right'])
eeg_epochs = epochs.copy().pick("eeg")
eog_epochs = epochs.copy().pick("eog").pick(['EOG-right'])
eog_data = eog_epochs.get_data()
eeg_data = eeg_epochs.get_data()
n_channels = eeg_data.shape[1]
def run_ica_experiment(_run, method_idx):
train_epochs = BCI_IV_Comp_Dataset.load_dataset(train_filepaths,
as_epochs=True,
concatenate=True,
drop_bad=True,
return_metadata=False,
tmin=-1.,
tmax=3.)
train_epochs.load_data().filter(l_freq=None, h_freq=40)
test_epochs_list, test_metadata_list = BCI_IV_Comp_Dataset.load_dataset(
test_filepaths,
as_epochs=True,
concatenate=False,
drop_bad=True,
return_metadata=True,
tmin=-1.,
tmax=3.)
all_methods = get_all_methods()
methods = all_methods if method_idx is None else [all_methods[method_idx]]
name = "" if method_idx is None else "_{}".format(all_methods[method_idx])
print("Using methods", methods)
print("Loaded test files:", len(test_epochs_list))
results = dict()
for method in methods:
print("Running for method", method)
results[method] = list()
clf = make_pipeline(PSD(fmin=0.1, fmax=40, picks=PICKS), Averager(),
MinMaxScaler(), SVC(C=4))
start = time.time()
ICA = get_ica_instance(method, n_components=ICA_N_COMPONENTS)
ICA.fit(train_epochs)
print("\tICA fitted!")
duration = time.time() - start
transformed_train_epochs = ICA.get_sources(train_epochs)
for i, (epochs,
mdata) in enumerate(zip(test_epochs_list, test_metadata_list)):
print("\t", i, mdata["id"])
epochs = epochs.copy().load_data().filter(l_freq=None,
h_freq=40).resample(90.)
transformed_test_epochs = ICA.get_sources(epochs)
scores = dict()
signal = np.hstack(transformed_test_epochs.get_data())
for fn_name in SCORING_FN_DICT:
score = apply_pairwise_parallel(signal,
SCORING_FN_DICT[fn_name])
scores[fn_name] = score
X_train, Y_train = transformed_train_epochs, transformed_train_epochs.events[:,
2]
X_test, Y_test = transformed_test_epochs, transformed_test_epochs.events[:,
2]
try:
clf.fit(X_train, Y_train)
except Exception as e:
print("\t\tFailed during fit:", str(e))
results[method].append({
"id": mdata["id"],
"score": None,
"bas": None,
"duration": duration
})
continue
pred = clf.predict(X_test)
bas = balanced_accuracy_score(Y_test, pred)
results[method].append({
"id": mdata["id"],
"score": scores,
"bas": bas,
"duration": duration
})
results_filepath = f"./results{name}.json"
with open(results_filepath, "w") as json_file:
json.dump(results, json_file, indent=4)
_run.add_artifact(results_filepath, content_type="json")
def run_ica_experiment(_run, method_idx):
# filepaths = Path(r"C:\Users\paull\Documents\GIT\BCI_MsC\notebooks\BCI_Comp_IV_2a\BCICIV_2a_gdf/").glob("*T.gdf")
dataset, metadata = BCI_IV_Comp_Dataset.load_dataset(filepaths,
as_epochs=True,
concatenate=False,
drop_bad=True,
return_metadata=True,
tmin=-1.,
tmax=3.)
all_methods = get_all_methods()
methods = all_methods if method_idx is None else [all_methods[method_idx]]
name = "" if method_idx is None else "_{}".format(all_methods[method_idx])
print("Using methods", methods)
results = dict()
for method in methods:
print("Running for method", method)
clf = make_pipeline(CSP(n_components=CSP_N_COMPONENTS), Vectorizer(),
MinMaxScaler(), SVC(C=4))
results[method] = list()
for i, (epochs, mdata) in enumerate(zip(dataset, metadata)):
print("\t", i, mdata["id"])
ICA = get_ica_instance(method, n_components=ICA_N_COMPONENTS)
start = time.time()
epochs = epochs.copy().load_data().filter(l_freq=None,
h_freq=40).resample(90.)
transformed_epochs = ICA.fit(epochs).get_sources(epochs)
duration = time.time() - start
scores = dict()
signal = np.hstack(transformed_epochs.get_data())
for fn_name in SCORING_FN_DICT:
score = apply_pairwise_parallel(signal,
SCORING_FN_DICT[fn_name])
scores[fn_name] = score
X, Y = transformed_epochs.get_data(), transformed_epochs.events[:,
2]
del epochs, transformed_epochs
try:
clf.fit(X, Y)
except Exception:
print("\t\tFailed during fit")
results[method].append({
"id": mdata["id"],
"score": None,
"bas": None,
"duration": duration
})
continue
pred = clf.predict(X)
bas = balanced_accuracy_score(Y, pred)
results[method].append({
"id": mdata["id"],
"score": scores,
"bas": bas,
"duration": duration
})
results_filepath = f"./results{name}.json"
with open(results_filepath, "w") as json_file:
json.dump(results, json_file, indent=4)
_run.add_artifact(results_filepath, content_type="json")