first commit - committing all file to repository

audio_feedback.py

+import pandas as pd
+import constants as c
+import sleep_stage_classifer as s
+
+
+def check_pip(tester_list):
+    last_probas = [{k: v for k, v in elem.items() if k != 'Stage'} for elem in tester_list[-c.CHECK_LEN:]]
+    conditions_met_count = sum((elem['N2'] + elem['N3'] > 0.85) and (elem['N3'] <= 0.5) for elem in last_probas)
+    if len(last_probas) < c.CHECK_LEN:
+        return False
+    return conditions_met_count >= c.CHECK_LEN-1        #egy híján mindre igaz
+

constants.py

+#data/device managing
+
+SAMPLING_FREQ = 250     #mintavételi freq, ez stabil, írja, ha megváltozik, nem változtatni
+SIGNAL_TIME = 350       #ha időre fut a kód, akkor a start signal után az input() helyére sleep(...)
+RESIST_TIME = 5
+EPOCH_LEN = 30         #30 s-nyi epochokkal dolgozik
+YASA_INPUT_LEN = 2*60*7        #percben (az epoch 30 s)
+
+#EEG metadata values
+
+MALE = True
+AGE = 22
+
+#file managing
+
+FILE_IDENTIFIER = "full_night_full_time"
+FILE_FOLDER_NAME = "C:\Programkornyezet\PythonProjects\AudiostimulationProject\AudiostimulationDirectory\output_files"
+SHOW_FEEDBACK = True
+
+#audiofeedback decision
+
+CHECK_LEN = 3
+

device_controller.py

+"""
+BrainBit eszköz kezelése, adatok fogadása,
+kommunikáció az eszközzel.
+
+Ezt futtatni!
+
+Az on_signal_recieved-ből lehet kinyerni a beérkező adatot. Specifikációja a leírásában.
+"""
+from neurosdk.scanner import Scanner
+from neurosdk.cmn_types import *
+import concurrent.futures
+from time import sleep
+import constants as c
+import sleep_stage_classifer as s
+import file_manager as f
+
+def sensor_found(scanner, sensors):
+    """
+    Kiíratja az összes hatókörön belüli szenzort
+    """
+    for index in range(len(sensors)):
+        print('Sensor found: %s' % sensors[index])
+
+def on_sensor_state_changed(sensor, state):
+    """
+    Ha a szenzor állapota megváltozik (pl.: disconnect) kiírja
+    """
+    print('Sensor {0} is {1}'.format(sensor.name, state))
+
+def on_battery_changed(sensor, battery):
+    """
+    Amikor a szenzor töltöttségi állapota megváltozik kiírja az értékét
+    """
+    print('Battery: {0}'.format(battery))
+
+def on_signal_received(sensor, data):
+    """
+    Ha beérkezik jel a szenzorból, továbbadja
+    és elindítja a feldolgozást.\n
+    A sleep_stage_classifiernek adjuk itt át a 'data' beérkező adatot.\n
+    Fontos! az adatok páronként érkeznek, de minden elem megfelel egy mérésnek.
+    A PackNum, ami meghatározza melyik adat érkezett, kettőnél megegyezik (250 Hz-en biztosan),
+    egy lista két eleme, ilyen listákból áll a 'data'. Pl.: 2 s alatt 250 Hz-en 250 PackNum-nyi adat érkezik,
+    de ez 500 mintát jelent, a frekvencia rendben van tehát. Ezeket később választjuk szét.
+    """
+    s.epoch_maker(data)
+
+def on_resist_received(sensor, data):
+    """
+    Ha ellenállást mérünk, amikor új adat érkezikkiírja Ohm-ban az egyes elektródák értékeit.
+    """
+    print(data)
+
+try:
+    """
+    Irányítja a kezelést
+    
+    - Inicializálja a szenzort, és 5 s-ig keresi a hatókörben. A scanner csak eddig fut. (Elég)
+    - Kiír minden információt, és létrehoz egy sensor-t, inicializálja a fenti függvényeket.
+    - Kiírja az alap adatokat, a sorozatszámot és frekvenciát a biztonság kedvéért
+    - Futtat egy constants-ban meghatározott idejű ellenállásmérést, kiírja (Vált. lehet)
+    - Futtatja az adatgyűjtést addig, amég input nem jön(felébredtünk) (enter), akkor leállítja
+    - Ekkor futtatja a fájlbamentést, ami egy globális változóból ment, csv fájlba. (file_manager)
+    - Ezt a globális listát biztonsági okokból töröljük
+    - Lecsatlakozik és eltávolítja a szenzort
+    """
+    scanner = Scanner([SensorFamily.LEBrainBit, SensorFamily.LEBrainBitBlack])
+
+    scanner.sensorsChanged = sensor_found
+    scanner.start()
+    print("Starting search for 5 sec...")
+    sleep(5)
+    scanner.stop()
+
+    sensorsInfo = scanner.sensors()
+    for i in range(len(sensorsInfo)):
+        current_sensor_info = sensorsInfo[i]
+        print(sensorsInfo[i])
+
+
+        def device_connection(sensor_info):
+            return scanner.create_sensor(sensor_info)
+
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future = executor.submit(device_connection, current_sensor_info)
+            sensor = future.result()
+            print("Device connected")
+
+        sensor.sensorStateChanged = on_sensor_state_changed
+        sensor.batteryChanged = on_battery_changed
+
+        sensFamily = sensor.sens_family
+
+        sensorState = sensor.state
+        if sensorState == SensorState.StateInRange:
+            print("connected")
+        else:
+            print("Disconnected")
+
+        print("sensor serial number:", sensor.serial_number) #az eszköz beazonosítása
+
+        if sensor.is_supported_parameter(SensorParameter.SamplingFrequency):
+            if sensor.sampling_frequency == sensor.sampling_frequency.FrequencyHz250:
+                print("sampling frequency:", 250, "Hz")
+            else:
+                print("sampling frequency changed:", sensor.sampling_frequency)
+
+        if sensor.is_supported_feature(SensorFeature.Resist):
+            sensor.resistDataReceived = on_resist_received
+
+        if sensor.is_supported_feature(SensorFeature.Signal):
+            sensor.signalDataReceived = on_signal_received
+
+        if sensor.is_supported_command(SensorCommand.StartResist):
+            sensor.exec_command(SensorCommand.StartResist)
+            print("Start resist")
+            sleep(c.RESIST_TIME)
+            sensor.exec_command(SensorCommand.StopResist)
+            print("Stop resist")
+
+        if sensor.is_supported_command(SensorCommand.StartSignal):
+            sensor.exec_command(SensorCommand.StartSignal)
+            print("Start signal")
+            input()
+            sensor.exec_command(SensorCommand.StopSignal)
+            print("Stop signal")
+            f.sleep_stage_file_maker()      #stop signal után menti el fájlba az eredményt
+            s.yasa_output_list.clear()      #biztonság kedvéért törli a nagy listát
+
+        sensor.disconnect()
+        print("Disconnect from sensor")
+        del sensor
+
+
+    del scanner
+    print('Remove scanner')
+except Exception as err:
+    print(err)

file_manager.py

+"""
+Az egész mérés adataiból csv fájl írása, a fájlok vizualizálása,
+feldolgozása
+
+Feldolgozáshoz beírni az analyze_file()-ba a fájl abszolút elérési útvonalát és ezt futtatni.
+"""
+
+import os
+import datetime
+import constants as c
+import sleep_stage_classifer as s
+import audio_feedback as a
+import pandas as pd
+import matplotlib.pyplot as plt
+import yasa
+
+def sleep_stage_file_maker():
+    """
+    Létrehoz egy csv. fájlt az mérés során felvett összes
+    adatból
+
+    A névkonvenció: hóvap_nap_file_identifier. Az atuális hónap nap, a file_identifiert pedig a
+    constants-ban lehet beállítani. (pl.:full_night, vagy test)
+
+    A mentés helye az output_files (C:\Programkornyezet\PythonProjects\AudiostimulationProject\AudiostimulationDirectory\output_files)
+    DataFrame-ből csv írása, mentődnek a:
+    prediktált alvási fázisok
+    és az egyes alvási fázisok valószínűségei minden 30s epochra.
+    Pl egy sor: W, 0.22, 0.4, 0.28, 0.01, 0.99 (5 fázis: N1, N2, N3, R, W)
+
+    """
+    current_date = datetime.datetime.now().strftime("%m_%d")
+    file_name = f"{current_date}_{c.FILE_IDENTIFIER}.csv"  # A fájl neve most tartalmazza a dátumot
+    file_path = os.path.join(c.FILE_FOLDER_NAME, file_name)  # Célmappa megadása
+
+    yasa_output_df = pd.DataFrame(s.yasa_output_list)
+    yasa_output_df.to_csv(file_path, index=False)
+
+
+def analyze_file(csv_file_path):
+    """
+    Fájl feldolgozása, statisztika, valószínűségi plot,
+    hisztogram.
+
+    - A csv.-t feldolgozzuk, külön DataFrame-be kerülnek a betűvel jelzett 'Stage'-k és az 5 oszlopnyi 'Probability'.
+    - Ábrázoljuk egy 'yasa' parancssor alapján a hisztogramot. Méretét az adatok mennyisége határozza meg.
+    - Ábrázoljuk 'matplotlib'-bel a valószínűségeket (ahogy a 'yasa'-ban is, de az fájlból nem működik). Méretét az
+      adatmennyiség határozza meg.
+    - Ábrázoljuk az összes szükséges értéket és kimutatást 'yasa' függvény segítségével.
+    :param csv_file_path: output_list-be mentett .csv fájlok abszolút elérési útvonala
+    """
+    df = pd.read_csv(csv_file_path)
+    stages = df['Stage']
+    probabilities = df.iloc[:, 1:]  # Az első oszlop a 'Stages', a többi a valószínűsége
+    stages_int = yasa.hypno_str_to_int(stages)
+
+    if c.SHOW_FEEDBACK:
+        dict_list = df.to_dict(orient='records')
+        pip_results = [a.check_pip(dict_list[:i + 1]) for i in range(len(dict_list))]
+
+    # Hipnogram ábrázolása
+    plt.figure(figsize=(int(len(stages)/40), 5))
+    hypn = yasa.Hypnogram(stages, freq="30s")
+    hypn.plot_hypnogram(lw=2, fill_color="whitesmoke")
+    plt.show()
+
+    # Valószínűségplot ábrázolása
+    colors = ["#99d7f1", "#009DDC", "xkcd:twilight blue", "xkcd:rich purple", "xkcd:sunflower"]
+    ax = probabilities.plot(kind='area', figsize=(int(len(probabilities)/6), 5), stacked=True, color=colors, alpha=0.8, linewidth=0)
+    ax.set_xlabel('Time (30-sec epoch)')
+    ax.set_ylabel('Probability')
+    ax.legend(frameon=False, bbox_to_anchor=(1, 1))
+
+    if c.SHOW_FEEDBACK:
+        for i, result in enumerate(pip_results):
+            if result:  # Ha True, akkor kiemeljük az adott szakaszt
+                ax.axvspan(i, i + 1, color='yellow', alpha=0.5)  # Kiemelés sárga színnel
+    plt.show()
+    print((sum(pip_results)/2), "percnyi ingerlés volt összesen")
+
+    # Alvási statisztikák kiíratása táblázatban
+    statistics = yasa.sleep_statistics(stages_int, sf_hyp=1/30)
+    stat_df = pd.DataFrame(list(statistics.items()), columns=['Parameter', 'Value'])
+    plt.figure(figsize=(4, 6))
+    plt.title('Sleep Statistics')
+    plt.axis('off')
+    plt.table(cellText=stat_df.values, colLabels=stat_df.columns, loc='center', cellLoc='center', colColours=['lightgray'] * 2)
+    plt.tight_layout()
+    plt.show()
+
+ #A függvény használata (r-el kezdődjön), csak akkor kikommentelni, ha ezt akarjuk!:
+analyze_file(r'C:\Programkornyezet\PythonProjects\AudiostimulationProject\AudiostimulationDirectory\output_files\02_14_full_night_full_time.csv')
+

output_files/01_28_probak probaja.txt

+Megjegyzsek: Valami komment

output_files/01_30_test.txt

+Megjegyzsek: 
+Alvs hossza: 300 msodperc
+Alvsstdiumok:
+WWWWWWWWN3N3
\ No newline at end of file

output_files/01_31_full_night.txt

+Megjegyzsek: 
+Alvs hossza: 27600 msodperc
+Alvsstdiumok:
+WWWN1WWWRN1N1N1WRRWWWWWWWWWWWRWWRRWWN1N1N2N2N2N2N2N2WWRRN2N1N1N2N2N2WWWN2N1N2N2N2N2N2WWWN2N2N2N2N2N2N3N2N2N2N2N2N2N2N2N3N3N2N2N2N2N2N2N2WN2N2WN3N2WN2N2N2N2N2N3N1N2N2N2N2N2N2N3N3WN1N3N2N2N2N2N2RRN2WN2N2N2N2N2N3N3N3N3N3WN2N2N2N2N2N3N2N3N3N2N2N2N2N2N2N2RRN1N2N2N2N2N2N3RRRWWWN1N2N1N2N2N1N3WN2N2N2N2N2N2N2N2N2N1N2N2N2N2N1WN1N1WWWN2N2N2N1N1N1WN1WRWN2N1N1N1N1N2WN2RRRN2N1N2WWWWN2N1N2N2N2N1RRRWN2N2N2N2N2N3N2N2WWWRN2N2N2N2N3N1N2WWRN2N2N2N2N3N3N2N3N3N2N2N2N2N2N2RRWN3N1N1N2N2N3N3N3N2WWN2N2N2N2N2N3WWWN3N3N3N2N2N2N2N2WWWN2N2N2N1N2N3N3N3WN1N2N2N3N1RRRN1N3N2N2N2N2N2WN3RWRN1N1N2N2N2N2N2N2N2N3N3N3N2N2N2N2N1N1RWRRN2N2N2N2N2N2N3N2N3N1N2N2N2N2N2N1WN3N2N2N2N2N2RWWRWN2N2N2N2N2WN2N2RRN1N1N1N2N2N1WWWWWN1N1N2N2N2N2N2WWWN1RN1N2N2N1N2N2WN1N2N2N1N1N1RN1WWN2N2WN1N1RN2N2N2WN2N1N2N1N1RRN1N1WWN1N1N1N1N2N1N1N1RRRRN2N2N2N2N2N2WWN2N2N2N2N2N2WWWN1RN2N2N2N2N2WN2WN2N2N2N1N2N2N2N2RWN2N2N2N2WN2N2N2N2RWRRN2N2N2N2N2WN2N2N2RN2RN2N2N1WWWN1N2N2N2N2N2N2N2WN1N2N2N2N2N2N2N2N2WN2N2N2N2N2N2N2N2N2N1N1N1N2N2N2N2N2N2WWN2N2N2N2N2N2N2N2N2N1N1N2N2RN2N2WN2N2N2WWWN2N1WRRWN1N1WN1N1N1N2RWWWN1N1N1N1RRRN1RWWWN2N1RN1N1N1RN2WRRN1N1N1N2WWWWN2N1N2N2N1RRRWWN2N2N2RRN1WRWN1N1N1N2WRRWN1WWN1N1N1N2RRRRWRN2N2N1N2N1N1N2N2N2N2N2N2N2N2N2N2N2N2N2N2WN2N2WN2N2WN2WWN2N2N2N2N2N2N2N2WWN1N2N2N2N2N3N2N2N3N2N2N2N2N2N2N2N2N2N1WRRN2N2N1N2N2N2WN2N3N2N2N2N2N2N2WWN2N2N2RN2N2N2RWWN2N2N2N2RN1N1N1RN2N2N1N2N1N1RRN1RRWN1N1N1N1WN1WWWWRRRWN1N1N1N2WN1WN1N1N1N1N1N1WWN2N1N1N2RN1N1RRWN2N2N1N1RRRRRN1N2N1N2N2RRRN1WWWWRRRN2N2N2N2N2N2N1N2N2N2N2N2N2RWWN2N2N2N2N2N2N2N2WWWN2N1N2N2N2N2N2RRRN2N2N2N2N2N1WN2N2RRN2N2N2N2N2RN2N2N2N2N2N2N2N2N2N2
\ No newline at end of file

output_files/02_07_proba.csv

+,Stages,Wake,N1,N2,N3,REM

output_files/02_08_proba.csv

+Stage,N1,N2,N3,R,W
+W,0.002025900648336483,0.009979557415976325,0.01171355587644056,2.224687916083924e-05,0.9762587391800857
+W,2.90158196275357e-09,0.024423642771819798,0.10950051368826261,0.0001899210398244758,0.8658859195985111
+N3,0.022629665001471472,0.0818853683578004,0.7496875233967508,0.00022653612552841834,0.14557090711844894
+W,0.008276845851208843,0.11888112668369767,0.02332754183335283,0.0005157720289213804,0.8489987136028192
+W,0.03565482372045853,0.2597570083894267,0.07319620642229158,0.04324808554239315,0.5881438759254299
+W,0.009909968480305929,0.0634486010217303,0.06080684116391874,0.005219620796238941,0.8606149685378061
+W,0.1325512293352536,0.17271264929839975,0.01325466198179207,0.026014960183161302,0.6554664992013932
+W,0.0452223865262344,0.02315421216180819,0.0029222264245234767,0.03318452375773362,0.8955166511297002
+W,0.07035429645396082,0.039163804415101915,0.0069106393995640725,0.06928877013710229,0.8142824895942709
+W,0.05706552239486159,0.019662641664345212,0.015049185872325205,0.13045439865920944,0.7777682514092585
+W,0.022214476601400485,0.010829513694268671,0.002176552723987714,0.024006585761255025,0.940772871219088
+W,0.013626923605200866,0.018888202378959884,0.048230677270222136,0.1045728586331831,0.814681338112434
+W,0.030042626901077986,0.023422322997999893,0.003766560318475127,0.19041392351710382,0.7523545662653432
+W,0.12420056125338974,0.07242519839480432,0.01027438412066539,0.1971833879198383,0.5959164683113022
+W,0.07362903273024068,0.14334823383055598,0.005939035893297939,0.14178703983673788,0.6352966577091675
+W,0.040093618528286634,0.25681086985324986,0.003851931858316699,0.0880761513234036,0.6111674284367432
+W,0.013322951984131488,0.09576162884364431,0.025098390989393322,0.015532215536175682,0.8502848126466552
+W,0.04865500127878461,0.060733899410997126,0.0028260428289169615,0.03171378252458794,0.8560712739567135
+W,0.07563164640230227,0.057876159266785715,0.002413070899277222,0.015361138304666629,0.8487179851269682
+W,0.06172424360603551,0.12759625351251108,0.003352094489946202,0.014816265336526399,0.7925111430549808
+N2,0.12942806582614869,0.42989853281764756,0.002959224517117996,0.05415795739799791,0.3835562194410879
+W,0.015249566165468955,0.31457743399852633,0.03541343141594838,0.05507987518633362,0.5796796932337226
+W,0.00043362124545577363,0.021987195363591058,0.05869162653674065,0.44459286984228114,0.47429468701193145
+W,3.720779500613049e-10,0.02967606716796003,0.009012937072576058,0.22938968609833404,0.731921309289052
+W,0.0024112053714663057,0.04657764663995931,0.017850162600028613,0.3623411354846584,0.5708198499038873
+W,0.020026101916746503,0.1461429391443934,0.005885004950519741,0.3217309447498832,0.5062150092384571
+N2,0.028595156159133474,0.39403794494200395,0.016274804968531197,0.32235105537924763,0.23874103855108372
+N2,0.018132742009970643,0.7587611755093091,0.03133407623450841,0.06057257306697668,0.13119943317923508
+W,5.947481702839215e-07,0.326557845075159,0.10543430949916606,0.09586988231098459,0.47213736836652015
+W,0.04340181479816643,0.33593855131310785,0.009485715391254495,0.2604771312185694,0.35069678727890197

output_files/02_13_time_test.csv

+Stage,N1,N2,N3,R,W
+W,0.0008895853014080202,0.0022713948453392005,0.00034338871852268213,4.167058199082962e-05,0.9964539605527394
+W,0.05509204365403952,0.15931451506262417,0.026786944292761315,0.00047481625235089157,0.7583316807382241
+W,0.06771030655650585,0.19492434226657993,0.043607584728122095,0.0010693858259889898,0.6926883806228032
+W,0.044448641191602314,0.17029671180553144,0.020858943797353348,0.003043816877363092,0.7613518863281498
+W,0.0874437311371782,0.23925601072631272,0.10941383766002957,0.0052147036136014095,0.5586717168628782
+N2,0.06625930738082794,0.48555234087904203,0.04131852858985634,0.034431256143415585,0.372438567006858
+W,0.047914516428623856,0.24173388442174745,0.003589538249468626,0.14388643412602029,0.5628756267741398
+W,0.015221256711729313,0.0878632771963969,0.03151975531016059,0.06835409907802316,0.79704161170369
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sleep_stage_classifer.py

+"""
+A beérkező adatokat epochokra bontja, és alkalmazza a 'yasa' alvási fázis
+osztályozó metódust
+
+- A 'YASA' egy open-source előtanított osztályozó metóus: https://raphaelvallat.com/yasa/build/html/index.html
+- Egy epoch-lista folyamatosan töltődik (30s-nyi adat a frek függvényében)
+- Egy lista, ami alapján a 'yasa' klasszifikál majd folyamatosan töltődik ebből, nagysága állítható, optimalizálandó
+- Ezt a bemeneti listát raw EDF változóvá alakítjuk a szabványok szerint, mert ezt tudja kezelni a 'yasa'
+
+  Megtörténik a klasszifikáció, ennek három kimenete van:
+    - Printeli a prediktált értéket (betűjelek) amikor elkészül, tehát kb. 3    0 s-onkékt.
+    - Feltölt egy globális listát, amiből majd a fájl megíródik.
+    - Az aktuális érték továbbadódik az audio_feedbacknak, ami döntést hoz ez alapján a hangstimulációról
+
+
+"""
+
+import yasa
+import mne
+import numpy as np
+import time
+import constants as c
+import audio_feedback as a
+
+
+epoch_data_list = []  # egy epochnyi adat
+yasa_input_list = []  # a yasa-ba bemenő adatmennyiség
+yasa_output_list = []  # a fájlba írandó lista folyamatos bővüléggel
+first_call = True  # az első 5 perc flagje a yasa_classifier-ben
+
+
+def yasa_classifier(raw):
+    """
+    A beérkező nyers EEG adatokból meghatározza epochonként az
+    alvási fázist és a valószínűségeket
+
+    - A yasa.SleepStaging parancs tölti be az EDF adatokat a sls ('yasa'-ba töltött (változtatható nagyságú)
+      adathalmazt tartalmazó saját típusú) változóba. A 'yasa' kizárólag EGY! elektródával működik,
+      ezért célszerű egy central-derivationt megadni (változtatható). Az alany metaadatai a constants-ban
+      változtathatóak. Ez után a
+
+        - .predict() fél perces epochok készítése után a fázisoknak megfelelő betűjelek listájával tér vissza.
+        - .predict_proba() az ugyanezen 5 s-es epochoknál az egyes alvási fázisokhoz tartozó valószínűségekkel
+          (5 fázis: N1, N2, N3, R, W) tér vissza.
+    - A 'yasa' esetéban minél hosszabb időtartamot elemez annál jobb az eredmény, 5 percnél kevesebbet nem tud,
+      így az első 5 percet meg kell várni, utána tudjuk epochonként megkapni az értékeket.Ezt kezeli az if/else.
+      Az első 5 perc esetén kiírjuk az egész listát, utána, fél percenként ablakolt adatok érkeznek, az ezekből készült
+      listából az utolsó elem szükséges/új nekünk. több adat a pontos mérést szolgálja.
+    - Az if/else-n belül a prediktálás eredményei, és a valószínűségek, mint dictionary
+      a yasa_output_list globális változóhoz adódnak, amiből a file_maker a teljes mérés végén csv. fájlt készít.
+    - Kiírja az aktuális értéket
+    - Meghívja majd az audio_feedbacket az érték alapján, ami majd meghatározza az audiostimulációt.
+    :param raw: EDF fájl
+    """
+    start_time = time.time()
+    global yasa_output_list, first_call
+    sls = yasa.SleepStaging(raw, eeg_name="O1-O2", metadata=dict(age=c.AGE, male=c.MALE))
+    y_pred = sls.predict()
+    y_proba = sls.predict_proba()
+
+    if first_call:
+        for i in range(len(y_pred)):
+            y_pred_i = y_pred[i]
+            y_proba_i = y_proba.iloc[i].to_dict()
+            yasa_output_list.append({"Stage": y_pred_i, **y_proba_i})
+            print(y_pred_i)
+        first_call = False
+    else:
+        y_pred_last = y_pred[-1]
+        y_proba_last = y_proba.iloc[-1].to_dict()
+        yasa_output_list.append({"Stage": y_pred_last, **y_proba_last})
+        print(y_pred_last)
+
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    print(
+        f"Eltelt idő: {elapsed_time} s, y_pred hossza: {len(y_pred)}\n")  # Eltelt idő és az y_pred hosszának kiírása
+
+
+def raw_object_maker(yasa_input_list):  #névkonvenció? nemtudom
+    """
+    A beérkező adatokból a 'yasa' számára feldolgozható
+    EDF fájlt készít
+
+    - Az eszközből a yasa_input_list-be töltött adatok párosával (2 elemű listaként) érkeznek. Ezt először kezelni kell.
+    - Létrehozzuk az egyes elektródák értékeit tároló változókat, és ebbe töltjük az összes értéket,
+      így a dupla elemek megszűnnek
+    - Elvégezzük az EDF formátum megvalósításához szükséges formázásokat
+    - Elkészítünk central-derivationokat, később ezeket az EEG csatornákat lehet a 'yasa' klasszifikálónak beadni,
+      a kapott és kiszámolt EEG adatok listáival, az EEG nevekkel és a mintavételi
+      frekvenciával létrehozzuk az EDF fájlt.
+    - Meghívjuk a yasa_classifier függvényt
+    :param yasa_input_list: Az apoch_maker által meghatározott méretűbrainBitből érkező lista (dupla elemekkel)
+    """
+    o1_data = []
+    o2_data = []
+    t3_data = []
+    t4_data = []
+
+    for pair in yasa_input_list:
+        for obj in pair:
+            o1_data.append(obj.O1)
+            o2_data.append(obj.O2)
+            t3_data.append(obj.T3)
+            t4_data.append(obj.T4)
+
+    o1_np = np.array(o1_data)
+    o2_np = np.array(o2_data)
+    t3_np = np.array(t3_data)
+    t4_np = np.array(t4_data)
+
+    o1_o2_derivation = o2_np - o1_np
+    t3_t4_derivation = t4_np - t3_np
+
+    eeg_data = np.array([o1_np, o2_np, t3_np, t4_np, o1_o2_derivation, t3_t4_derivation])
+
+    ch_names = ['O1', 'O2', 'T3', 'T4', 'O1-O2', 'T3-T4']
+    ch_types = ['eeg'] * 6
+    sfreq = c.SAMPLING_FREQ
+    info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
+    raw = mne.io.RawArray(eeg_data, info)
+    yasa_classifier(raw)
+
+
+def epoch_maker(data):
+    """
+    A beérkező nyers adatból elkészíti a szükséges méretű listát, amelyet továbbít közvetetten a
+    klasszifikálónak
+
+    A listák méretei itt változtathatóak meg!
+
+    - A globális változók a többszöri meghívás miatt szerepelnek:
+    - Az epoch_data_list egy-egy epoch hosszát határozza meg. Ez ugyan a constants-ban változtatható, de ne változtass,
+      a sampling_freq (250 Hz) és az epoch_len (30 s) is adott. /2 a duplikált bemeneti adatok miatt fontos, megoldása
+      a raw_object_maker-ben.
+    - A beérkező adat hozzáadódik az epoch_data_list-hez. Ez az alapegységünk, 30 s-nyi adat! Ha ez "megtelik"
+      hozzáadódik a yasa_input_list-hez, ez az (epoch méret egész többszörösére) változtatható hosszúságú lista,
+      amit a 'yasa' klasszifikáló egészben kap meg. Most 15 perc, minél hosszab, annál pontosabb a klasszifikálás,
+      de nő a feldolgozási idő is.
+    - A YASA_INPUT_LEN a constants-ban változtatható méretű.
+
+      - A yasa-nak szükséges legalább 5 perc, ezért az első 5 percet egyben adjuk át, utána epochonként.
+      - Amikor "megtelik" az első epoch törlődik, és egy új hozzáadódik, ablakolunk.
+
+
+    :param data: BrainBit-ből érkező nyers adat
+    """
+    global epoch_data_list, yasa_input_list
+    epoch_data_list_len = int(c.SAMPLING_FREQ * c.EPOCH_LEN / 2)
+
+    epoch_data_list.append(data)
+
+    if len(epoch_data_list) >= epoch_data_list_len:
+        yasa_input_list.extend(epoch_data_list)
+        epoch_data_list = []
+
+        if len(yasa_input_list) > (c.YASA_INPUT_LEN) * epoch_data_list_len:
+            del yasa_input_list[:epoch_data_list_len]
+
+        if len(yasa_input_list) > epoch_data_list_len * 10:
+            raw_object_maker(yasa_input_list)