This example shows how to extract the epochs from the dataset of a given subject and then classify them using Machine Learning techniques using Riemannian Geometry. The code also creates a figure with the spectral embedding of the epochs. """ # Authors: Pedro Rodrigues <*****@*****.**> # # License: BSD (3-clause) import warnings warnings.filterwarnings("ignore") # define the dataset instance dataset = AlphaWaves( ) # use useMontagePosition = False with recent mne versions # get the data from subject of interest subject = dataset.subject_list[0] raw = dataset._get_single_subject_data(subject) # filter data and resample fmin = 3 fmax = 40 raw.filter(fmin, fmax, verbose=False) raw.resample(sfreq=128, verbose=False) # detect the events and cut the signal into epochs events = mne.find_events(raw=raw, shortest_event=1, verbose=False) event_id = {'closed': 1, 'open': 2} epochs = mne.Epochs(raw,
Riemannian Geometry. The code also creates a figure with the spectral embedding of the epochs. """ # Authors: Pedro Rodrigues <*****@*****.**> # # License: BSD (3-clause) import warnings warnings.filterwarnings("ignore") # define the dataset instance direc = '../Data/AlpData/' subject = 'subject_004.mat' filepath = direc + subject dataset = AlphaWaves() # get the data from subject of interest raw = dataset._get_single_subject_data(filepath) # filter data and resample fmin = 3 fmax = 40 raw.filter(fmin, fmax, verbose=False) raw.resample(sfreq=128, verbose=False) # detect the events and cut the signal into epochs events = mne.find_events(raw=raw, shortest_event=1, verbose=False) event_id = {'closed': 1, 'open': 2} epochs = mne.Epochs(raw, events,
""" This module provides an interface between end-point API (api.py) and classification method. It interprets the request and call the appropriate classification method. In addition, it creates also the dataset instances. """ dataset_2012 = BrainInvaders2012(Training=True) dataset_2013 = BrainInvaders2013(NonAdaptive=True, Adaptive=False, Training=True, Online=False) dataset_2014a = BrainInvaders2014a() dataset_2014b = BrainInvaders2014b() dataset_2015a = BrainInvaders2015a() dataset_2015b = BrainInvaders2015b() dataset_alpha = AlphaWaves(useMontagePosition=False) dataset_vr = VirtualReality(useMontagePosition=False) dataset_phmd = HeadMountedDisplay(useMontagePosition=False) def run_request(str_request): store = Store() request_and_keywords = interpret(str_request) request = request_and_keywords['request'] # Keywords are used to conduct independant requests on the store. # Such results will be returns along with the classification results. keywords = request_and_keywords["keywords"] result = {} if ('bi2012' in request): params = request['bi2012'] score = classification.classify_2012(dataset_2012, params, store)