Assignment_4.ipynb

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    "# Assignment 4: EEG and Event-Related Potentials\n",
    "Please submit this assignment to Canvas as a jupyter notebook (.ipynb).  The assignment will get you working with EEG brain data, completing several analyses on event-related potentials."
   ]
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    "# imports\n",
    "import cmlreaders as cml\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from ptsa_plot.ptsa_plot.topo import topoplot\n",
    "import pandas as pd\n",
    "from scipy.stats import ttest_rel\n",
    "from scipy.stats import ttest_ind\n",
    "from mne.stats import bonferroni_correction\n",
    "from mne.stats import fdr_correction"
   ]
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    "## Assignment Overview\n",
    "In this assignment you will carry out basic time-series analyses of electroencephalographic\n",
    "(EEG) data. Specifically, you will measure event-related potentials (ERPs) associated with item\n",
    "encoding events in a free recall experiment. For background on the ERP method please review\n",
    "Chapter 3 of EHM.\n",
    "\n",
    "In the previous assignment you learned the basics of working with EEG data\n",
    "and generating ERPs for a single subject. Now you will generate ERPs across subjects,\n",
    "calculate statistical significance, and investigate the distribution of ERP voltage across the scalp.\n",
    "\n",
    "As you'll soon figure out, the process of dealing with large data sets can be quite challenging. Just loading large amounts of data can take a long time.\n",
    "To help deal with the scale of the data sets, here are several recommended strategies:\n",
    "\n",
    "1. Have the mindset of testing all your code on subsets of the data and then once you've worked things out, testing on large subsets.  To perform initial debugging of errors, test with e.g., a single session from a single subject. To perform more meaningful correctness testing, test with 10 to 15 subjects for instance rather than all of them. This will give you enough data to start to see meaningful patterns emerge for the largest effects in the data but not take nearly as much time.\n",
    "\n",
    "2. Another recommended strategy on this topic is thinking about creating optimized and reusable functions. For many problems, you'll write code to perform some task. Then in subsequent problems, you'll be assigned largely similar tasks.  For these subsequent problems, avoid the temptation to copy and paste large blocks of code that are 99% the same. Put the original code in a function and add flags to indicate that e.g., you now want to use a different variant of the function with a conditional statement internal to the function rather than creating a whole new function. This will make your code much easier to read, maintain, and debug going forward.\n",
    "\n",
    "3. Save intermediate results (e.g., computed ERPs for each subject) as e.g. pickle files (see the pickle library in Python for how to save and load these files, which can store most Python objects) or as .npy (numpy) files for raw array data (less recommended). This way if (and when) you have to restart your kernel you can load up those results without having to reload all the data and recompute the same results. Do not save all the EEG data to your local directory, only intermediate and final analysis results.\n",
    "\n",
    "4. Save final results, such as matplotlib figures (which seaborn uses under the hood), as PDF and PNG files with either fig.savefig() or plt.savefig(), or again with the Python pickle module for other results.\n",
    "\n",
    "For the following analyses:\n",
    "* Use subjects with subject IDs between LTP093 and LTP330 (inclusive; not every number in that range is assigned to a subject) which have all 24 sessions available. \n",
    "* Exclude the 24th session (session numbers are zero-indexed, i.e., they count from zero to 23 so exclude sessions with session number 23) from these analyses since a different version of the experiment was run in that session. \n",
    "* Some subjects have artifactual recordings with much higher signal variance than others. These subjects need to be excluded from your analyses. Exclude subjects with maximum ERP values above 0.00005 V (the scalp EEG recordings are measured in volts).\n",
    "* Some subjects will have different sample rates for different sessions. When you attempt to average/concatenate over the time series or ERPs for these sessions with different sample rates, an error will be thrown for shape misalignment. You can try resampling the EEG with \n",
    "\n",
    "    `sess_eeg = sess_eeg.resampled(sample_rate) where sess_eeg is a PTSA.TimeSeries with EEG`\n",
    "\n",
    "    You'll ultimately want to make sure that each of the EEG traces/ERPs you're averaging/concatenating has the same length and set of time values. As long as the time series lengths after resampling are the same, you can just directly set the time values in the PTSA array to be the same, e.g., with\n",
    "\n",
    "    `sess_eeg['time'] = time`\n",
    "\n",
    "    where `time` is the most common set of time values for the EEG traces, i.e., running from -200 ms to 1000 ms in increments to 2 ms (500 Hz sampling rate). You can exclude sessions that don't align in time after resampling."
   ]
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       "      <td>LTP393</td>\n",
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       "                                             all_events experiment  \\\n",
       "487   protocols/ltp/subjects/LTP093/experiments/ltpF...     ltpFR2   \n",
       "488   protocols/ltp/subjects/LTP093/experiments/ltpF...     ltpFR2   \n",
       "489   protocols/ltp/subjects/LTP093/experiments/ltpF...     ltpFR2   \n",
       "490   protocols/ltp/subjects/LTP093/experiments/ltpF...     ltpFR2   \n",
       "491   protocols/ltp/subjects/LTP093/experiments/ltpF...     ltpFR2   \n",
       "...                                                 ...        ...   \n",
       "6526  protocols/ltp/subjects/LTP393/experiments/ltpF...     ltpFR2   \n",
       "6527  protocols/ltp/subjects/LTP393/experiments/ltpF...     ltpFR2   \n",
       "6528  protocols/ltp/subjects/LTP393/experiments/ltpF...     ltpFR2   \n",
       "6529  protocols/ltp/subjects/LTP393/experiments/ltpF...     ltpFR2   \n",
       "6530  protocols/ltp/subjects/LTP393/experiments/ltpF...     ltpFR2   \n",
       "\n",
       "     import_type                                        math_events  \\\n",
       "487        build  protocols/ltp/subjects/LTP093/experiments/ltpF...   \n",
       "488        build  protocols/ltp/subjects/LTP093/experiments/ltpF...   \n",
       "489        build  protocols/ltp/subjects/LTP093/experiments/ltpF...   \n",
       "490        build  protocols/ltp/subjects/LTP093/experiments/ltpF...   \n",
       "491        build  protocols/ltp/subjects/LTP093/experiments/ltpF...   \n",
       "...          ...                                                ...   \n",
       "6526       build  protocols/ltp/subjects/LTP393/experiments/ltpF...   \n",
       "6527       build  protocols/ltp/subjects/LTP393/experiments/ltpF...   \n",
       "6528       build  protocols/ltp/subjects/LTP393/experiments/ltpF...   \n",
       "6529       build  protocols/ltp/subjects/LTP393/experiments/ltpF...   \n",
       "6530       build  protocols/ltp/subjects/LTP393/experiments/ltpF...   \n",
       "\n",
       "     original_session  session subject subject_alias  \\\n",
       "487                 0        0  LTP093        LTP093   \n",
       "488                 1        1  LTP093        LTP093   \n",
       "489                10       10  LTP093        LTP093   \n",
       "490                11       11  LTP093        LTP093   \n",
       "491                12       12  LTP093        LTP093   \n",
       "...               ...      ...     ...           ...   \n",
       "6526                5        5  LTP393        LTP393   \n",
       "6527                6        6  LTP393        LTP393   \n",
       "6528                7        7  LTP393        LTP393   \n",
       "6529                8        8  LTP393        LTP393   \n",
       "6530                9        9  LTP393        LTP393   \n",
       "\n",
       "                                            task_events  \n",
       "487   protocols/ltp/subjects/LTP093/experiments/ltpF...  \n",
       "488   protocols/ltp/subjects/LTP093/experiments/ltpF...  \n",
       "489   protocols/ltp/subjects/LTP093/experiments/ltpF...  \n",
       "490   protocols/ltp/subjects/LTP093/experiments/ltpF...  \n",
       "491   protocols/ltp/subjects/LTP093/experiments/ltpF...  \n",
       "...                                                 ...  \n",
       "6526  protocols/ltp/subjects/LTP393/experiments/ltpF...  \n",
       "6527  protocols/ltp/subjects/LTP393/experiments/ltpF...  \n",
       "6528  protocols/ltp/subjects/LTP393/experiments/ltpF...  \n",
       "6529  protocols/ltp/subjects/LTP393/experiments/ltpF...  \n",
       "6530  protocols/ltp/subjects/LTP393/experiments/ltpF...  \n",
       "\n",
       "[2470 rows x 9 columns]"
      ]
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   ],
   "source": [
    "exp = 'ltpFR2'                      # LTPFR2 experiment\n",
    "df = cml.get_data_index('ltp')\n",
    "df = df.query(\"experiment == @exp\")     # select only LTPFR2 sessions\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04db1c53-2763-4245-badf-f43d958ab3ad",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "Let's extend the ERP analyses of serial positions from the previous assignment to a larger group of subjects. \n",
    "\n",
    "1) Create the between-subjects average ERP for serial positions 1, 2, 3, and 4-24 (averaged) for all ltpFR2 subjects.  Plot the 4 ERPs on the same graph and make sure to label each line in a legend.\n",
    "2) Compare the group average to the individual subject ERP analysis in the previous assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cf36c190-69cd-40a7-8a1a-ccfc6e8fb16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 1.1\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b6d88d-6bdb-491e-921d-12aeb322553d",
   "metadata": {},
   "source": [
    "Question 1.2\n",
    "\n",
    "**YOUR CODE HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b0abfe-f9e0-4cfe-bd3b-0105fc025e95",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "\n",
    "1) Compute 95% confidence bands separately for each time point of each ERP. Plot the ERPs with the time-varying confidence intervals. \n",
    "    * Recall that the 95% confidence interval is 1.96 * standard error of the mean, where the standard error is defined as: \n",
    "    $$\n",
    "    SE = \\frac{\\sigma}{\\sqrt{n}}\n",
    "    $$\n",
    "    where $\\sigma$ is the sample standard deviation and $n$ is the number of independent observations (here we should treat subjects as independent).\n",
    "\n",
    "2) Do the confidence bands exclude zero at any points? Which ones? Are there points where they do not overlap? What is the statistical interpretation of these confidence intervals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d8b58b2-badc-49e6-b3af-5f05646105e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 2.1\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc57d434-3971-4f2a-9178-fd7eb28db97a",
   "metadata": {},
   "source": [
    "Question 2.2\n",
    "\n",
    "**YOUR CODE HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986fd9bc-5d27-4a0f-b566-2fcf09d51a4c",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "1) For each subject, compute the difference between the ERPs for serial position 1 and for the average of serial positions 4-24 (i.e. ERP_1 minus ERP_4_24) and average the difference ERPs across subjects. Now compute the 95% confidence band around the difference waveform, and plot.\n",
    "2) Does this exclude zero at any points? Do these results match those from the previous analysis? If not, explain the difference in the inference one can make from these confidence bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "72379657-fe11-40a2-bb5c-90a6a29ddab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 3.1\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2759f174-3846-4b39-93a6-8221a77255ca",
   "metadata": {},
   "source": [
    "Question 3.2\n",
    "\n",
    "**YOUR CODE HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fbc333-211b-44e7-ae94-ce0c01878d93",
   "metadata": {},
   "source": [
    "## Inferential Statistics on and Topography of ERPs\n",
    "Now let's use some statistical methods to analyze our ERPs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978d9c78-b920-4644-a0b6-530414cfad24",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Question 4\n",
    "1) Compute an across-subject paired t-test (scipy.stats.ttest_rel), comparing at each time point the voltage of items in serial position 1 to the average of those in serial positions 4-24. Use a horizontal bar to indicate which time points exceed the criterion of p < 0.05 without correcting for multiple comparisons. \n",
    "2) How do these results match up with those in the previous two exercises?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "712228e2-0e2d-4b26-bb5f-aceb30064b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 4.1\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b107d6c-4a76-4465-80f5-07a89524eaa9",
   "metadata": {},
   "source": [
    "Question 4.2\n",
    "\n",
    "**YOUR ANSWER HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875930b8-1cab-4bd5-a32c-4c2eb266ab91",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "1) Recompute the statistical significance of the difference between ERPs using the false-discovery rate (FDR) method described in Chapter 3 (specifically you may use the Benjamini and Hochberg correction method). Indicate the points with FDR-corrected p-values p < 0.05 using a bar of another color shifted above or below the first bar. \n",
    "2) Comment on the difference between the two methods. Which is more conservative and why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1fd32164-24f0-440c-bc02-6b28643f1858",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 5.1\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b8f968-6506-4429-b47c-030c0d272359",
   "metadata": {},
   "source": [
    "Question 5.2\n",
    "\n",
    "**YOUR ANSWER HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be7c5c0-8a4a-4eab-acf5-9f95e06f25ff",
   "metadata": {},
   "source": [
    "## Question 6\n",
    "1) In all of the previous exercises you computed each ERP at each time point separately. Here we will compute the ERP averaged over a time window of 300-800 ms post stimulus (resulting in a single averaged value across the entire time interval) using the same procedures as for a single time point (i.e., baseline correction, etc.) again comparing serial position 1 to the average of serial positions 4-24. For this interval-averaged ERP, compute the across-subjects paired t-test for electrode 75 (Oz) and report the t-value, the degrees of freedom, and the p-value (as in t(df) = xxx, p = 0.xx).\n",
    "2) Repeat this analysis for the interval from 100-300 ms post-stimulus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4708b65e-a140-431b-a491-d557e323d2b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 6.1\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9095ae8f-a7f9-4e0f-b75b-f67da3d7a99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 6.2\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f71887-29fc-494c-96f9-987b26f7c8b9",
   "metadata": {},
   "source": [
    "## Question 7\n",
    "To visualize the ERP serial position effect across the entire scalp we will now repeat the preceding exercise at all electrodes and generate “head plots” (ptsa_plot.topoplot) showing the difference in voltage across all electrodes (red colors indicating positive values, i.e., serial position 1 > serial positions 4-24; blue colors indicating negative values). Below we give example code for using ptsa_plot.topoplot with arbitrary example values. Create two versions of this plot:\n",
    "\n",
    "1) One where you mark all electrodes for which this difference is significant using a conventional t-test without correction for multiple comparison using a significance threshold of p < 0.05. \n",
    "2) For the second plot only mark those electrodes that survive a correction for multiple comparisons using the FDR method. \n",
    "3) Compare the differences between the corrected and uncorrected statistical tests here with those for individual time points which you calculated earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5513fc24-f63a-445b-9fae-398b5badb40c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2af434ec1990>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example code for using ptsa_plot.topoplot\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# load channel plot coordinates\n",
    "with open('./ptsa_plot/ptsa_plot/electrode_layouts/HCGSN128.dat', 'r') as sensorfile:\n",
    "    angles, radii = sensorfile.readlines()\n",
    "    sensors = (np.r_[np.array([float(a)\n",
    "                               for a in angles.strip().split()]), 0],\n",
    "               np.r_[[float(r) for r in radii.strip().split()], 0])\n",
    "\n",
    "# examples channels for demonstration\n",
    "n_channels = 128\n",
    "channel_indices = np.arange(n_channels)\n",
    "\n",
    "# just channels in our loaded EEG; use this for your actual plots\n",
    "# channel_indices = np.array([np.int(c.values.tolist()[1:]) for c in list_eeg['channel']]) - 1 # keep this minus 1 for alignment\n",
    "# n_channels = len(channel_indices)\n",
    "\n",
    "cleaned_sensors = tuple(s[channel_indices] for s in sensors)\n",
    "\n",
    "vmax = 0.6\n",
    "vmin = -vmax\n",
    "\n",
    "# create example values to plot\n",
    "values = np.arange(n_channels) / n_channels * (vmax - vmin) + vmin\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "# plot smoothed heat map of sensor values with sensors plotted as dots\n",
    "topoplot(sensors=cleaned_sensors, values=values,\n",
    "         axes=axs[0], cmap='bwr', sensor_props={'marker': '.'}, \n",
    "         vmin=vmin, vmax=vmax, levels=np.linspace(vmin, vmax, 50),\n",
    "         labels=np.array(['x'  if v > np.mean(values) else ' ' for v in values]))\n",
    "axs[0].set_title('plot 1')\n",
    "\n",
    "# another example plot\n",
    "topoplot(sensors=cleaned_sensors, values=-values,\n",
    "         axes=axs[1], cmap='bwr', sensor_props={'marker': '.'}, \n",
    "         vmin=vmin, vmax=vmax, levels=np.linspace(vmin, vmax, 50),\n",
    "         labels=np.array(['x'  if v < np.mean(values) else ' ' for v in values]))\n",
    "axs[1].set_title('plot 2')\n",
    "\n",
    "#### Add legend to the plots\n",
    "marker_dot = plt.plot([], marker=\".\", ls=\"\", color=\"purple\")[0]\n",
    "marker_x = plt.plot([], marker=\"x\", ls=\"\", color=\"green\")[0]\n",
    "\n",
    "fig.legend([marker_dot, marker_x], \n",
    "           ['All sensors', 'Special sensors'],\n",
    "           loc='center', bbox_to_anchor=(0.5, -0.05), ncol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c546286f-6ff5-4b6d-ae3b-311ffe6e3b17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 7.1\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "89029e40-9ba1-4786-a0de-02db59a60f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 7.2\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b690d8f6-9688-4764-8470-6f77f24ab0bf",
   "metadata": {},
   "source": [
    "Question 7.3\n",
    "\n",
    "**YOUR ANSWER HERE**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "workshop_311",
   "language": "python",
   "name": "workshop_311"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}