minor change

.gitignore

@@ -8,7 +8,6 @@ __pycache__/
 # ignore some files related to ML models
 models
 DataiBand
-out_QS
 train
 *.joblib
 *.npy
@@ -42,6 +41,7 @@ var/
 *.egg
 .vs/
 
+
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.

Automatic_EM_detection_yasa.py

@@ -0,0 +1,57 @@
+import mne
+import numpy as np
+import matplotlib.pyplot as plt
+import yasa
+from mne.time_frequency import tfr_morlet, tfr_multitaper
+%matplotlib qt
+
+data_path = "P:\\3013102.01\\Data\\"
+participant_session = ['NL_DNDRS_0004__ses-2']
+
+for idx, c_subj in enumerate(participant_session):
+    
+    participant_number = c_subj.split('__')[0]
+    session_number = c_subj.split('__')[1]
+    
+    # Reading EEG data
+    path_EEG_L = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG L.edf'
+    path_EEG_R = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG R.edf'
+
+    # Load the EDF file
+    raw_EEG_L = mne.io.read_raw_edf(path_EEG_L, preload=True)
+    raw_EEG_R = mne.io.read_raw_edf(path_EEG_R, preload=True)
+    
+    raw_EEG_L.filter(l_freq=.3, h_freq=30)
+    raw_EEG_R.filter(l_freq=.3, h_freq=30)
+
+    raw_EEG_L_get_data = np.ravel(raw_EEG_L.get_data(units="uV"))
+    raw_EEG_R_get_data = np.ravel(raw_EEG_R.get_data(units="uV"))
+    
+    loc = raw_EEG_L_get_data[7190*256:]
+    roc = raw_EEG_R_get_data[7190*256:]
+    
+    plt.plot(loc)
+    plt.plot(roc)
+
+    REM_events = yasa.rem_detect(loc = loc, roc = roc, sf=256, hypno=None, include=4, amplitude=(30, 325), duration=(0.1, 1.2), freq_rem=(0.2, 8), remove_outliers=True, verbose=False)
+    REM_events.summary()
+    REM_events.plot_average(time_before=1, time_after=1);
+
+        # Let's get a boolean mask of the REMs in data
+    mask = REM_events.get_mask()
+    
+    loc_highlight = loc * mask[0, :]
+    roc_highlight = roc * mask[1, :]
+
+    loc_highlight[loc_highlight == 0] = np.nan
+    roc_highlight[roc_highlight == 0] = np.nan
+    
+    plt.figure(figsize=(16, 4.5))
+    plt.plot(loc, 'slategrey', label='LOC')
+    plt.plot(roc, 'grey', label='ROC')
+    plt.plot( loc_highlight, 'indianred')
+    plt.plot( roc_highlight, 'indianred')
+    plt.xlabel('Time (seconds)')
+    plt.ylabel('Amplitude (uV)')
+    plt.title('REM sleep EOG data')
+    plt.legend()

export-import-instruction.txt

@@ -0,0 +1,5 @@
+ ******** Create an environment from YAML file *************
+
+conda env create --name dreamento --file dreamento.yml
+
+conda env create --name offlineDreamento --file offlineDreamento.yml