{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dcc9ed72-23d0-4228-ac2a-0da7dcbf8181",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T12:34:46.355313Z",
     "start_time": "2024-10-07T12:34:46.350508Z"
    },
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    "jupyter": {
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   "source": [
    "Fieldtrip: Resting State analysis pipeline\n",
    "==========================================\n",
    "\n",
    "Authors: Hadi Zaatiti <hadi.zaatiti@nyu.edu>, Karima Raafat, <karima.raafat@nyu.edu>\n",
    "\n",
    "This notebook is to be run in MATLAB, while having fieldtrip library installed.\n",
    "It is a pipeline for processing the `Resting State` experiment raw data acquired from KIT-MEG system at NYUAD, run frequency analysis in sensor space.\n",
    "The main outcome is a comparison plot of the power of the alpha-band (8-14Hz) for two conditions: eyes closed and eyes open.\n",
    "\n",
    "A higher alpha-band power should be observed in the eyes closed data segment than in eyes open.\n",
    "\n",
    "The `Resting State` code experiment in Psychtoolbox can be found here:\n",
    "\n",
    "[Resting State Experiment page](https://meg-pipeline.readthedocs.io/en/latest/3-experimentdesign/experiments/1-exp-resting-state.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34dce04-09e6-43a7-877d-b632e24dcc3c",
   "metadata": {},
   "source": [
    "Importing data \n",
    "--------------\n",
    "\n",
    "The data used in this notebook is hosted on `NYU BOX`. Permissions are given upon request.\n",
    "\n",
    "- Install the BOX app from [here](https://www.box.com/resources/downloads)\n",
    "- Set an environment variable with name `MEG_DATA` to the path of the Data folder e.g.,\n",
    "    - `C:\\Users\\user_name\\Box\\MEG\\Data`\n",
    "    - or `C:\\Users\\user_name\\Box\\Data\n",
    "\n",
    "MATLAB setup\n",
    "------------\n",
    "\n",
    "Make sure that:\n",
    "- Fieldtrip is installed in MATLAB\n",
    "- Add to MATLAB path the custom-made functions for NYUAD MEG lab found [here](https://github.com/Hzaatiti/meg-pipeline/tree/main/pipeline/field_trip_pipelines/matlab_functions)\n",
    "\n",
    "Each experiment run using the KIT system generates a `.con` file and two or more .mrk files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70cf61ae-91d4-4377-819b-76eb3aa789d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%% Resting state task pipeline\n",
    "\n",
    "MEG_DATA_FOLDER = getenv('MEG_DATA');\n",
    "\n",
    "TASK_NAME = 'resting-state\\';\n",
    "\n",
    "% Set path to KIT .con file of sub-03\n",
    "DATASET_PATH = [MEG_DATA_FOLDER, TASK_NAME];\n",
    "\n",
    "%THis needs fixing to sav eproperly\n",
    "SAVE_PATH = [MEG_DATA_FOLDER, TASK_NAME];\n",
    "\n",
    "SYSTEM  = 'meg-kit2\\';\n",
    "SUB_ID = 'sub-01\\';\n",
    "\n",
    "CLOSED_EYES = [DATASET_PATH, SUB_ID, SYSTEM, 'closed_resting_01.con'];\n",
    "\n",
    "OPEN_EYES = [DATASET_PATH, SUB_ID, SYSTEM, 'open_resting_02.con'];"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9b2be4-e8e6-410a-9906-c0464d5c64a1",
   "metadata": {},
   "source": [
    "Preprocess the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66b86af6-0f84-4d32-b1e4-dbea3e8e9947",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------\n",
      "FieldTrip is developed by members and collaborators of the Donders Institute for Brain,\n",
      "Cognition and Behaviour at Radboud University, Nijmegen, the Netherlands.\n",
      "\n",
      "                          --------------------------\n",
      "                        /                            \\\n",
      "                     ------------------------------------\n",
      "                    /                                    \\\n",
      "          -------------------------------------------------\n",
      "         /                            /\\/\\/\\/\\/\\ \n",
      "         ---------------------------------------------------\n",
      "                  |        F  i  e  l  d  T  r  i  p       |\n",
      "                  ------------------------------------------\n",
      "                   \\                                      /\n",
      "                     ------------------------------------\n",
      "                          \\            /\n",
      "                            ----------\n",
      "\n",
      "Please cite the FieldTrip reference paper when you have used FieldTrip in your study.\n",
      "Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen. FieldTrip: Open\n",
      "Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.\n",
      "Computational Intelligence and Neuroscience, vol. 2011, Article ID 156869, 9 pages, 2011.\n",
      "doi:10.1155/2011/156869.\n",
      "-------------------------------------------------------------------------------------------\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 1 from 1\n",
      "the call to \"ft_preprocessing\" took 10 seconds\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 1 from 1\n",
      "the call to \"ft_preprocessing\" took 4 seconds\n"
     ]
    }
   ],
   "source": [
    "%% Preprocess data\n",
    "\n",
    "% Preprocess the MEG data\n",
    "cfg = [];\n",
    "cfg.dataset = CLOSED_EYES;\n",
    "cfg.coilaccuracy = 0;\n",
    "data_CLOSED_EYES = ft_preprocessing(cfg);\n",
    "\n",
    "% Preprocess the MEG data\n",
    "cfg = [];\n",
    "cfg.dataset = OPEN_EYES;\n",
    "cfg.coilaccuracy = 0;\n",
    "data_OPEN_EYES = ft_preprocessing(cfg);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8cf031-b9f8-44b8-aa0e-1bcdac6e2657",
   "metadata": {},
   "source": [
    "We can now define two trials, one for eyes closed and one for eyes open."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d467d45b-c803-434e-953a-3e51af77fdff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating trial function 'ft_trialfun_general'\n",
      "reading the header from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\resting-state\\sub-01\\meg-kit2\\closed_resting_01.con'\n",
      "reading the events from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\resting-state\\sub-01\\meg-kit2\\closed_resting_01.con'\n",
      "found 1 events\n",
      "created 1 trials\n",
      "the call to \"ft_definetrial\" took 5 seconds\n",
      "using the events from the configuration structure\n",
      "the following events were found in the data:\n",
      "\n",
      "event type: 'combined_binary_trigger' \n",
      "with event values : 1\n",
      "these values occur: 1 times\n",
      "\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 1 from 1\n",
      "the call to \"ft_preprocessing\" took 1 seconds\n"
     ]
    }
   ],
   "source": [
    "  %% Define trials and segment the data: CLOSED EYES\n",
    "\n",
    "    previewTrigger = data_CLOSED_EYES.trial{1}(225, :);\n",
    "\n",
    "    MIN_trig = min(previewTrigger);\n",
    "    MAX_trig = max(previewTrigger);\n",
    "\n",
    "    threshold = (max(previewTrigger) + min(previewTrigger)) / 2;\n",
    "\n",
    "    cfg = [];\n",
    "    cfg.dataset  = CLOSED_EYES;\n",
    "    cfg.trialdef.eventvalue = 1; % placeholder for the conditions\n",
    "    cfg.trialdef.prestim    = 0; % 1s before stimulus onset\n",
    "    cfg.trialdef.poststim   = 200; % 1s after stimulus onset\n",
    "    cfg.trialfun = 'ft_trialfun_general';\n",
    "    cfg.trialdef.chanindx = 225;\n",
    "    cfg.trialdef.threshold = threshold;\n",
    "    cfg.trialdef.eventtype = 'combined_binary_trigger'; % this will be the type of the event if combinebinary = true\n",
    "    cfg.trialdef.combinebinary = 1;\n",
    "\n",
    "    CFG_TRIALS_CLOSED_EYES = ft_definetrial(cfg);\n",
    "    \n",
    "    ft_trialfun_show(CFG_TRIALS_CLOSED_EYES);\n",
    "\n",
    "    SG_CLOSED_EYES = ft_preprocessing(CFG_TRIALS_CLOSED_EYES);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ebc9ea-231d-447e-b01b-8e910d9c0baf",
   "metadata": {},
   "source": [
    "The open eyes data is the whole dataset, therefore to define this trial we will not be using any trigger channel, but rather define the trial from the 38000 to 238000 sample. (Sampling Frequency is 1kHz)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39f45451-c73b-4fc3-9732-6c8af5464b18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 1 from 1\n",
      "the call to \"ft_preprocessing\" took 9 seconds\n"
     ]
    }
   ],
   "source": [
    "    %% Define trials and segment the data: OPEN EYES\n",
    "    % This segment is a whole trial on its own, we do not need to use a trigger channel for it\n",
    "cfg = [];\n",
    "cfg.dataset = OPEN_EYES;\n",
    "hdr = ft_read_header(cfg.dataset);\n",
    "cfg = [];\n",
    "cfg.dataset = OPEN_EYES;\n",
    "start_sample = 38000;  % Start from the first sample\n",
    "end_sample = 238000;  % End at the last sample (or specify your duration in samples)\n",
    "\n",
    "\n",
    "cfg.trl = [start_sample end_sample 0];  % 0 offset means no time shift\n",
    "\n",
    "% Proceed to preprocess using the defined trial\n",
    "SG_OPEN_EYES = ft_preprocessing(cfg);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "22c4dcf4-a4ff-4022-b7ea-0e82c7ed0427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating layout from cfg.grad\n",
      "creating layout for yokogawa208 system\n",
      "the call to \"ft_prepare_layout\" took 0 seconds\n",
      "reading layout from file CTF151_helmet.mat\n",
      "reading 'layout' from file 'CTF151_helmet.mat'\n",
      "the call to \"ft_prepare_layout\" took 0 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%% Create KIT sensor Layout\n",
    "\n",
    "% This is a custom made function by Osama Abdullah to create the KIT sensor\n",
    "% layout at NYUAD\n",
    "kit_layout = create_kit_layout(CLOSED_EYES);\n",
    "\n",
    "% Plot the KIT sensors layout\n",
    "\n",
    "figure('Position', [100, 100, 1000, 800]); % Adjust the width and height (1000 and 800) as needed\n",
    "ft_plot_layout(kit_layout, 'box', 1);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c71f65-d07e-4a35-aa2d-e0c22b21456e",
   "metadata": {},
   "source": [
    "We will pick one sensor that is in the Occipital lobe, the `AG205`, compute the Fast Fourier Transform (FFT) for each trial type (eyes closed, eyes open) for the measurement provided by this sensor.\n",
    "The data is segmented by overlapping `Hanning` windows, FFT is computed for each window then averaged across the whole trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "206b5f5c-4b65-4dac-8fa6-ee84a49d7ff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the input is raw data with 256 channels and 1 trials\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n",
      "processing trial 1/1 nfft: 200000 samples, datalength: 200000 samples, 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n",
      "the input is raw data with 256 channels and 1 trials\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n",
      "processing trial 1/1 nfft: 200001 samples, datalength: 200001 samples, 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 0 seconds\n"
     ]
    }
   ],
   "source": [
    "%% FFT analysis in alpha band\n",
    "\n",
    "SENSOR_NAME = 'AG205';\n",
    "\n",
    "% FFT configuration for the first segment (2-30 Hz), for a specific channel\n",
    "cfg = [];\n",
    "cfg.method = 'mtmfft';              % Multitaper method for FFT\n",
    "cfg.foilim = [2 30];                % Frequency range: 2-30 Hz\n",
    "cfg.taper = 'hanning';              % Use Hanning taper for narrowband\n",
    "cfg.output = 'pow';                 % Output power spectrum\n",
    "cfg.channel = SENSOR_NAME;  % Specify the channel name (replace 'your_channel_name' with the actual name)\n",
    "freq_CLOSED_EYES = ft_freqanalysis(cfg, SG_CLOSED_EYES);\n",
    "\n",
    "% FFT configuration for the second segment (2-30 Hz), for the same specific channel\n",
    "cfg = [];\n",
    "cfg.method = 'mtmfft';              % Multitaper method for FFT\n",
    "cfg.foilim = [2 30];                % Frequency range: 2-30 Hz\n",
    "cfg.taper = 'hanning';              % Use Hanning taper for narrowband\n",
    "cfg.output = 'pow';                 % Output power spectrum\n",
    "cfg.channel = SENSOR_NAME;  % Specify the same channel name for the second segment\n",
    "freq_OPEN_EYES = ft_freqanalysis(cfg, SG_OPEN_EYES);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02dfc285-ad43-4d65-ae79-b502cb8b9cf4",
   "metadata": {},
   "source": [
    "We can now plot the Power spectrum and compare the difference in alpha-band power for the two conditions (Eyes closed, blue and Eyes open, red)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f63939eb-cd65-444d-904a-0902c6d0df0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%% Plot \n",
    "\n",
    "% Plot the power spectrum for Closed Eyes (for AG205)\n",
    "figure('Position', [100, 100, 1200, 800]);  % Adjust width and height to desired size\n",
    "plot(freq_CLOSED_EYES.freq, freq_CLOSED_EYES.powspctrm, 'b'); \n",
    "hold on;\n",
    "\n",
    "% Plot the power spectrum for Open Eyes (for AG205)\n",
    "plot(freq_OPEN_EYES.freq, freq_OPEN_EYES.powspctrm, 'r'); % \n",
    "\n",
    "% Customize the plot\n",
    "xlabel('Frequency (Hz)');\n",
    "ylabel('Power');\n",
    "title('FFT Power Spectrum for 8-14 Hz Band (Single Channel AG205)');\n",
    "legend('Closed Eyes', 'Open Eyes');\n",
    "xlim([2 30]); \n",
    "hold off;"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MATLAB Kernel",
   "language": "matlab",
   "name": "jupyter_matlab_kernel"
  },
  "language_info": {
   "file_extension": ".m",
   "mimetype": "text/x-matlab",
   "name": "matlab"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}