# -*- coding: utf-8 -*-
'''
Functions for randomly assigning participants as feedback participants or matched controls.
Generates a .txt file in the subject_path (folder named with the subjectID), denoting whether a participant
is feedback (first row containing 1) or control (first row containing 0)
'''
# Imports
import numpy as np
import pandas as pd
import os
import shutil
from settings import subject_path_init
subject_path = subject_path_init()
def copy_files(from_file, to_file):
'''
Copies .csv files containing directories to images to the subject folder and renames the files.
'''
shutil.copy(from_file+'_day_1.csv',to_file+'_day_1.csv')
shutil.copy(from_file+'_day_2.csv',to_file+'_day_2.csv')
shutil.copy(from_file+'_day_3.csv',to_file+'_day_3.csv')
shutil.copy(from_file+'_day_4.csv',to_file+'_day_4.csv')
shutil.copy(from_file+'_day_5.csv',to_file+'_day_5.csv')
def copy_alpha(from_file,to_file):
shutil.copy(from_file,to_file)
def read_re_move_indices(subjects_dir, sub, feedback_from=None):
'''
# -*- coding: utf-8 -*-
'''
Functions for finding EEG and marker (trigger points for stimuli onset, from experimental script: experimentFunctions.py) streams,
streaming and saving data, writing transcript files and changing system states for the neurofeedback system.
'''
# Imports
from pylsl import StreamInlet, resolve_stream, resolve_byprop
import csv
import time
import numpy as np
import sys
import settings
subject_path = settings.subject_path_init()
class data_init:
def __init__(self, fs, data_type, filename=None):
'''
# Arguments
fs: int
EEG sampling frequency in Hz.
data_type: string
'EEG' or 'marker' (trigger from experimental stimuli script, experimentFunctions.py)
filename: string
'''
self.fs, self.filename, self.data_type = fs, filename, data_type
#%%
#%matplotlib qt
import mne
import os
from mne_bids import write_raw_bids, BIDSPath,make_dataset_description
import re
import settings
import event_test
import pandas as pd
#%%
subjetc_file=settings.subject_path_init()
os.chdir(settings.base_dir_init())
#%%
raw_files=os.listdir(subjetc_file)
raw_file_list=[]
marker_lists=pd.read_csv('Behavioral/all_raw_marker.csv')['trigger'].to_list()
event_ids=event_test.raw_marker_create(marker_lists)
for raw_file in raw_files:
if os.path.splitext(raw_file)[1]=='.vhdr':
raw_file_list.append(raw_file)
#%%
for q in range(0,len(raw_file_list)):
raw=mne.io.read_raw_brainvision(os.path.join(subjetc_file,raw_file_list[q]),preload=False)
# if len(raw.info['ch_names'])>60:
# raw.info['bads']=['BIP1','BIP2','BIP3','BIP4','BIP5','BIP6','BIP7','BIP8','BIP9','BIP10','BIP11','BIP12','BIP13','BIP14',
# 'BIP15','BIP16','BIP17','BIP18','BIP19','BIP20','BIP21','BIP22','BIP23','BIP24']
# raw.drop_channels(ch_names=raw.info['bads'])
raw.info['line_freq'] = 50