def init(self):
"""
Global Constants
"""
# 1.INIT Parent Class
initbciclass.init(self)
# 2.Alter or add other vars
#INIT SAVING
self.TASK = 'PRIME' #
self.subtask = "ALLTASKS" #ALL TASKs in same FILE
#INIT PRESENTATION VARS
#Presentation PROTOCOL VARS
self.PROTOCOL_TYPE = "prime" #
self.PROTOCOL_DESIGN = "normal" #normal, test
#presentation init screen
self.INIT_TEXT = "Vamos iniciar a tarefa de Pre-treino:"
self.STIM_TIME = 60 * 3 #s
##EXPERIMENT VARS
#INIT prime Stimulus
#random seed
self.random_seed = None #None or no argument seeds from current time or from an operating system specific randomness source if available
#STIMULUS LIST
self.audiostimulus_l = [
"breathingv6.ogg", "imageryv6.ogg"
] #stimulus file name from maincode.stimulus folder
self.videostimulus_l = None
self.imagestimulus_l = None
def init(self):
"""
Global Constants
"""
#1.INIT Parent Class
initbciclass.init(self)
#2.Alter or add other functions
#INIT SAVING
self.TASK = "REST"
self.subtask = ""
#INIT PRESENTATION VARS
#Presentation PROTOCOL VARS- TODO: randomize protocol_feature
self.PROTOCOL_TYPE = "REST" #NFT or REST the functions will change it accordingly
self.PROTOCOL_DESIGN = "normal" #ABA or Normal -
self.STIM_TIME = 60 * 3 #60*2
def init(self):
"""
Global Constants
"""
#1.INIT Parent Class
initbciclass.init(self)
#2.Alter or add other functions
#INIT SAVING
self.TASK = 'REPORT'
self.subtask = ""
self.SAVE=False #always
#INIT PRESENTATION VARS
#Presentation PROTOCOL VARS- TODO: randomize protocol_feature
self.PROTOCOL_TYPE = "REPORT" # report
self.PROTOCOL_DESIGN = "normal" #ABA or Normal -
#presentation init screen
self.INIT_TEXT = "TAREFA: RESPONDER AOS QUESTIONÁRIOS \n\n\nPor favor, pegue nos questionários indicados e preencha. \n\n\nAssim que preencher, continue com as tarefas neste computador após clicar no enter."
#stimulus screen(No Stim)
self.STIMULUS_TEXT ="\n\n\n\n\n\n\n\n\nCarregando tarefa....."
self.STIM_TIME = 0.5 #s
def init(self):
"""
Global Constants
"""
#1.INIT Parent Class
initbciclass.init(self)
#2.Alter or add other functions
#INIT SAVING
self.TASK = 'NFT' # NFT
self.subtask = "" #ALL TASKs in same FILE
#INIT PRESENTATION VARS
#Presentation PROTOCOL VARS- TODO: randomize protocol_feature
self.PROTOCOL_TYPE = "" #NFT or REST the functions will change it accordingly
self.PROTOCOL_DESIGN = "normal" #ABA or Normal -
#presentation init screen
self.INIT_TEXT = ""
self.STIM_TIME = 30 #s
##EXPERIMENT VARS
self.datatdir = my.get_test_folder(foldername="e2_data")
self.subjectdir = "EG/S001" #
self.fileid = "online_result" #id that identifies the file
#protocol features of NFT
self.PROTOCOL_FEATURES_L = ["SMR", "upper_alpha"] #List
self.PROTOCOL_FEATURE = "SMR" #SMR or upper_alpha
self.random_seed = None #None or no argument seeds from current time or from an operating system specific randomness source if available
#OFFLINE PROCESSING -
#reward and inhibit bands - dict objects to buffer band features
self.initial_reward_bands, self.initial_inhibit_bands = {}, {
} #initial bands
self.reward_bands, self.inhibit_bands = {}, {
} #state bands - updated during online neurofeedback
def init(self):
"""
Global Constants
"""
#1.INIT Parent Class
initbciclass.init(self)
#2.Alter or add other functions
#INIT SAVING
self.TASK = 'NFT' # NFT
self.subtask = "" #ALL TASKs in same FILE
#INIT PRESENTATION VARS
#Presentation PROTOCOL VARS- TODO: randomize protocol_feature
self.PROTOCOL_TYPE = "" #NFT or REST the functions will change it accordingly
self.PROTOCOL_DESIGN = "normal" #ABA or Normal -
#presentation init screen
self.INIT_TEXT = ""
self.STIM_TIME = 30 #s
##EXPERIMENT VARS
#protocol features of NFT
self.PROTOCOL_FEATURES_L = ["SMR", "upper_alpha"] #List
self.PROTOCOL_FEATURE = "SMR" #SMR or upper_alpha
self.random_seed = None #None or no argument seeds from current time or from an operating system specific randomness source if available
#OFFLINE PROCESSING -
self.get_baseline_thresholds = False #play
self.acquire_baseline_data = False #True, get data or False, simulate
#reward and inhibit bands - dict objects to buffer band features
self.initial_reward_bands, self.initial_inhibit_bands = {}, {
} #initial bands
self.reward_bands, self.inhibit_bands = {}, {
} #state bands - updated during online neurofeedback
#ONLINE PROCESSING
#INIT PARAMETERS of nftalgorithm
#for advanced edition go to nftalgorithm.py
self.window_time = 1024 #ms window in ms to segment the data and process - NOTE:2^8 is better for n_fft (window to segment and epoching)
self.sampleRate = 1000 #Hz sample rate of the EEG amplifier
self.blocktime = 50 #ms multiple of block to add in window ring buffer (multiples of this value will be added) #NOTE: Because of online routine is ~250ms it will put >200ms online, and it's not constant
self.calculate_iaf = False #True/False - activate Individual Alpha Frequency calculation
self.show_inhibit_bands = False #True/False - show or not inhibit bands in feedback(goes to presentation)
#PREPROCESSING (https://en.wikipedia.org/wiki/Electroencephalography)
self.l_freq = 40 #hz - for limit see bands used for feature - Till 40 we let pass some muscle artifacts
self.h_freq = 1 #Hz - NOTE:Removes DC offset= constant freqs
self.subsample = None #None | hz - it interferes with fft
self.select_chs = [
'Fp1', 'Fp2', 'Fz', 'Pz'
] #chs used throught the processing #NOTE: Include Fp1 and Fp2 for rejection of blinks and saccades
self.ref_channels = [
] #rereferencing ; ref_channels= "average" | [] No-rereferencing | ['CH'] for rereference - WARNING:online is better to be Cz or Pz, not average
self.rej_max_peaktopeak = 100e-6 #V
self.rej_min_peaktopeak = 0.5e-6 #V
#PROCESSING
self.n_fft = 1024 #samples, window_time * sampleRAte
self.n_per_seg = self.n_fft * 1 #fft window size percentage
self.n_overlap = int(math.ceil(self.n_per_seg * 0.25))
self.pink_max_r2 = None #None #0.95 , None - doesnt check for noise in signal (#WARNING: if used it stops program)
self.threshold_reward_level = -0.38 #std (-0.38*std => 65% above)
self.threshold_inhibit_level = 1
self.feedback_reward_level = 0
self.feedback_inhibit_level = 0
#additional processing
self.adaptative_threshold_intask = None #s None| int seconds #update during task
self.adaptative_threshold_aftertask = True #update threshold at the end of the nfttask