def test_dataset_search_fun(self): print([ type(i).__name__ for i in utils.dataset_search('imagery', multi_session=True) ]) print([ type(i).__name__ for i in utils.dataset_search('imagery', multi_session=False) ]) l = utils.dataset_search( 'imagery', events=['right_hand', 'left_hand', 'feet', 'tongue', 'rest']) for out in l: print('multiclass: {}'.format(out.selected_events)) l = utils.dataset_search('imagery', events=['right_hand', 'feet'], has_all_events=True) for out in l: print('rh/f: {}, {}'.format( type(out).__name__, out.selected_events)) l = utils.dataset_search( 'imagery', events=['right_hand', 'left_hand', 'feet', 'tongue', 'rest'], total_classes=2) for out in l: print('two class: {}'.format(out.selected_events))
def test_dataset_channel_search(self): chans = ['C3', 'Cz'] All = utils.dataset_search('imagery', events=[ 'right_hand', 'left_hand', 'feet', 'tongue', 'rest']) has_chans = utils.dataset_search('imagery', events=[ 'right_hand', 'left_hand', 'feet', 'tongue', 'rest'], channels=chans) has_types = set([type(x) for x in has_chans]) for d in has_chans: s1 = d.get_data([1], False)[0][0][0] self.assertTrue(set(chans) <= set(s1.info['ch_names'])) for d in All: if type(d) not in has_types: s1 = d.get_data([1], False)[0][0][0] self.assertFalse(set(chans) <= set(s1.info['ch_names']))
def test_dataset_search_fun(self): found = utils.dataset_search("imagery", multi_session=True) print([type(dataset).__name__ for dataset in found]) found = utils.dataset_search("imagery", multi_session=False) print([type(dataset).__name__ for dataset in found]) res = utils.dataset_search( "imagery", events=["right_hand", "left_hand", "feet", "tongue", "rest"] ) for out in res: print("multiclass: {}".format(out.event_id.keys())) res = utils.dataset_search( "imagery", events=["right_hand", "feet"], has_all_events=True ) for out in res: self.assertTrue(set(["right_hand", "feet"]) <= set(out.event_id.keys()))
def datasets(self): if self.tmax is None: interval = None else: interval = self.tmax - self.tmin return utils.dataset_search( paradigm="p300", events=self.events, interval=interval, has_all_events=True )
def datasets(self): if self.tmax is None: interval = None else: interval = self.tmax - self.tmin return utils.dataset_search(paradigm='imagery', events=self.events, interval=interval, has_all_events=False)
def datasets(self): if self.tmax is None: interval = None else: interval = self.tmax - self.tmin return utils.dataset_search(paradigm='ssvep', events=self.events, total_classes=self.n_classes, interval=interval, has_all_events=False)
def test_dataset_search_fun(self): print([ type(i).__name__ for i in utils.dataset_search('imagery', multi_session=True) ]) print([ type(i).__name__ for i in utils.dataset_search('imagery', multi_session=False) ]) res = utils.dataset_search( 'imagery', events=['right_hand', 'left_hand', 'feet', 'tongue', 'rest']) for out in res: print('multiclass: {}'.format(out.event_id.keys())) res = utils.dataset_search('imagery', events=['right_hand', 'feet'], has_all_events=True) for out in res: self.assertTrue( set(['right_hand', 'feet']) <= set(out.event_id.keys()))
def test_dataset_channel_search(self): chans = ["C3", "Cz"] All = utils.dataset_search( "imagery", events=["right_hand", "left_hand", "feet", "tongue", "rest"] ) has_chans = utils.dataset_search( "imagery", events=["right_hand", "left_hand", "feet", "tongue", "rest"], channels=chans, ) has_types = set([type(x) for x in has_chans]) for d in has_chans: s1 = d.get_data([1])[1] sess1 = s1[list(s1.keys())[0]] raw = sess1[list(sess1.keys())[0]] self.assertTrue(set(chans) <= set(raw.info["ch_names"])) for d in All: if type(d) not in has_types: s1 = d.get_data([1])[1] sess1 = s1[list(s1.keys())[0]] raw = sess1[list(sess1.keys())[0]] self.assertFalse(set(chans) <= set(raw.info["ch_names"]))
pipe = make_pipeline(LogVariance(), clf) pipelines["AM+SVM"] = pipe ############################################################################## # Datasets # ----------------- # # 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
pipelines['AM + SVM'] = pipe ############################################################################## # Datasets # ----------------- # # 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', total_classes=2)) ########################################################################## # 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
def datasets(self): return utils.dataset_search(paradigm='imagery', events=self.events, has_all_events=True)
def datasets(self): return utils.dataset_search(paradigm='imagery', events=['right_hand', 'left_hand'], has_all_events=True)
def datasets(self): return utils.dataset_search(paradigm='imagery', total_classes=self.n_classes, has_all_events=True)
def datasets(self): return utils.dataset_search(paradigm='imagery')
from collections import OrderedDict from moabb.datasets import utils from moabb.analysis import analyze import mne mne.set_log_level(False) import logging import coloredlogs logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() coloredlogs.install(level=logging.DEBUG) datasets = utils.dataset_search('imagery', events=['supination', 'hand_close'], has_all_events=False, min_subjects=2, multi_session=False) for d in datasets: d.subject_list = d.subject_list[:10] paradigm = ImageryNClass(2) context = WithinSessionEvaluation(paradigm=paradigm, datasets=datasets, random_state=42) pipelines = OrderedDict() pipelines['av+TS'] = make_pipeline(Covariances(estimator='oas'), TSclassifier()) pipelines['av+CSP+LDA'] = make_pipeline(Covariances(estimator='oas'), CSP(8),