{ "cells": [ { "cell_type": "markdown", "id": "5e9a73d4-ccdb-4971-b8cb-34ffaee3cac6", "metadata": {}, "source": [ "# **--- `DatasetEphy`: container of neurophysiological data ---**\n", "---\n", "In this tutorial we're mainly going to see how to define a container hosting the neurophysiological data of one or several subjects." ] }, { "cell_type": "code", "execution_count": 1, "id": "6437bce7-384e-4779-b04d-6ca6a1b8be29", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import numpy as np\n", "import xarray as xr\n", "import pandas as pd\n", "\n", "from mne import EpochsArray, create_info\n", "\n", "from frites.dataset import DatasetEphy\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "f847437b-9ad9-4a48-85d6-2c4bb9aa605b", "metadata": {}, "source": [ "---\n", "# **--- ROOT PATH ---**\n", "\n", "

\n", "\n", "Define the path to where the data are located !\n", "

" ] }, { "cell_type": "code", "execution_count": 2, "id": "7f495bfb-ebcb-475f-97e9-d896eee27d9b", "metadata": {}, "outputs": [], "source": [ "ROOT = '../dataset/'" ] }, { "cell_type": "markdown", "id": "f0b2c43d-a26b-42f5-b246-fc9e2311457f", "metadata": { "tags": [] }, "source": [ "---\n", "# **--- Structure of the `DatasetEphy` ---**" ] }, { "cell_type": "markdown", "id": "ed6c707a-705c-46ec-ab92-ef3746e19839", "metadata": {}, "source": [ "## Signature of the `DatasetEphy`\n", "\n", "**`DatasetEphy(x, y='...', times='...', roi='...')`** where :\n", "---\n", "\n", "1. **`x` = The brain data**\n", " * **[Description] :** list containing the brain data of one or multiple subjects / sessions\n", " * **[Sizes] :** each element of the list is the epoched brain data. For example, for two subjects it would be : $[(n_{epochs}, n_{channels}, n_{times})_{subject_1}, (n_{epochs}, n_{channels}, n_{times})_{subject_2}]$\n", "2. **`y` = The external variable**\n", " * **[Description] :** list containing the external variable (e.g. stimulus type, behavioral model, reaction time etc.) of one or multiple subjects / sessions\n", " * **[Goal] :** the goal is then to ask if the variables `x` (brain data) and the external variable `y` shared information. Said differently, if there's differences in the brain activity according to the stimulus (\\~decoding) or if the brain data correlates with the behavioral model or reaction time (\\~regression)\n", " * **[Sizes] :** each element of the list is the external variable of a single subject. For example, the reaction time for two subjects : $[RT_{subject_1}, RT_{subject_2}] = [(n_{epochs},)_{subject_1}, (n_{epochs},)_{subject_2}]$\n", "3. **`times` = The time vector**\n", " * **[Description] :** a single time vector\n", " * **[Alternatives] :** in fact it can be anything, like frequencies (e.g. PSD)\n", "4. **`roi` = spatial dimension**\n", " * **[Description] :** list containing a spatial description of each subject (e.g. the name of the channels for i/M/EEG, the name of brain regions etc.)\n", " * **[Sizes] :** each element of the list describes the channels / ROI of a single subject. For example, still for two subjects : $[ROI_{subject_1}, ROI_{subject_2}] = [(n_{channels},)_{subject_1}, (n_{channels},)_{subject_2}]$\n", " \n", "## Good to know\n", "\n", "* It works for one / many subjects\n", "* It works for one / many sessions of a single subject (or animals like monkeys)\n", "* All subjects can have a different number of trials (or epochs) such as a different number of channels. The channels can also be located in different brain regions\n", "* However, the time vector **should be the same across all subjects or sessions**" ] }, { "cell_type": "markdown", "id": "198995b3-3365-4884-819c-0eab0302b495", "metadata": {}, "source": [ "---\n", "# **0 - Functions**" ] }, { "cell_type": "code", "execution_count": 3, "id": "7e600275-4fce-4022-90f0-8779c2fd534a", "metadata": {}, "outputs": [], "source": [ "###############################################################################\n", "###############################################################################\n", "# Load the data of a single subject\n", "###############################################################################\n", "###############################################################################\n", "\n", "def load_ss(subject_nb):\n", " \"\"\"Load the data of a single subject.\n", " \n", " Parameters\n", " ----------\n", " subject_nb : int\n", " Subject number [0, 12]\n", " \n", " Returns\n", " -------\n", " hga : xarray.DataArray\n", " Xarray containing the high-gamma activity\n", " anat : pandas.DataFrame\n", " Table containing the anatomical informations\n", " beh : pandas.DataFrame\n", " Table containing the behavioral informations\n", " \"\"\"\n", " print(f\"Loading the data of subject #{subject_nb}\")\n", "\n", " # load the high-gamma activity\n", " file_hga = os.path.join(ROOT, 'hga', f'hga_s-{subject_nb}.nc')\n", " hga = xr.load_dataarray(file_hga)\n", "\n", " # load the name of the brain regions\n", " file_anat = os.path.join(ROOT, 'anat', f'anat_s-{subject_nb}.xlsx')\n", " anat = pd.read_excel(file_anat)\n", "\n", " # load the behavior\n", " file_beh = os.path.join(ROOT, 'beh', f'beh_s-{subject_nb}.xlsx')\n", " beh = pd.read_excel(file_beh)\n", " \n", " return hga, anat, beh\n" ] }, { "cell_type": "markdown", "id": "ab7ea866-009c-451c-853b-8dc78017eb6f", "metadata": {}, "source": [ "---\n", "# **1 - Define a `DatasetEphy` using Xarray**\n", "## 1.1 Load the data of a single subject" ] }, { "cell_type": "code", "execution_count": 4, "id": "dbfa1b0b-545e-4be6-b8e7-8322b6dba0ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading the data of subject #2\n" ] } ], "source": [ "# load the data of subject #2\n", "subject_nb = 2\n", "hga, anat, beh = load_ss(subject_nb)" ] }, { "cell_type": "markdown", "id": "ce9a6045-3dc3-4da9-88fc-2a6df153600a", "metadata": {}, "source": [ "## 1.2 Define the `DatasetEphy`" ] }, { "cell_type": "code", "execution_count": 5, "id": "c5aae800-6ec0-4027-8ff8-fe8275b4cd92", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Definition of an electrophysiological dataset\n", " Dataset composed of 1 subjects / sessions\n", "\u001b[1m\u001b[1;33mWARNING\u001b[0m | Impossible to infer the sampling frequency. You should consider providing a time vector\n", "\u001b[1m\u001b[1;33mWARNING\u001b[0m | No time vector. A default one is created\n", "\u001b[1m\u001b[1;33mWARNING\u001b[0m | No regions of interest are provided (roi). Default ones are created\n", " At least False subjects / roi required\n", " Supported MI definition none (none)\n" ] }, { "data": { "text/html": [ "
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       "          1.9421154e+00,  2.9742542e-01,  1.2414060e+00],\n",
       "        [ 6.1670655e-01,  8.1772202e-01,  5.5597889e-01, ...,\n",
       "          8.2523727e-01,  7.1212858e-01,  7.8324312e-01]],\n",
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       "         -7.1455121e-01, -3.5409722e-01, -1.5684823e-02],\n",
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       "        [-1.8743157e-01, -2.1864200e-02, -1.8016915e-01, ...,\n",
       "         -2.6785204e-01, -3.0482829e-01, -3.8188598e-01]]], dtype=float32)\n",
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       "    y        (trials) int32 2kB 2 -2 -1 2 1 -1 -2 2 2 ... -1 2 2 -2 2 -2 -2 1 -1\n",
       "    subject  (trials) int64 4kB 3 3 3 3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3 3 3 3\n",
       "  * roi      (roi) object 16B "X'2-X'1" "X'4-X'3"\n",
       "    agg_ch   (roi) int64 16B 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
       "    sfreq:         64.0\n",
       "    __version__:   0.4.4\n",
       "    modality:      electrophysiology\n",
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       "    y_dtype:       int\n",
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       "    mi_type:       cd\n",
       "    mi_repr:       I(x; y (discret))\n",
       "    agg_ch:        1\n",
       "    multivariate:  0
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       "         -9.12338123e-02,  5.90627594e-03,  1.95374191e-01],\n",
       "        [ 1.24216640e+00, -8.37370306e-02, -6.17846131e-01, ...,\n",
       "          6.05997205e-01,  8.87233794e-01,  7.83706903e-01],\n",
       "        [ 5.40017366e-01,  1.42501104e+00,  1.25638211e+00, ...,\n",
       "          1.89581943e+00,  8.78425837e-01,  9.62470412e-01],\n",
       "        [-1.77959085e-01, -3.04039836e-01, -3.52950811e-01, ...,\n",
       "         -5.81773818e-01, -3.64884168e-01, -1.08975649e-01],\n",
       "        [-5.28543703e-02, -2.01798141e-01, -6.21015489e-01, ...,\n",
       "         -4.49335366e-01,  9.38852131e-02,  1.04526430e-01],\n",
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       "         -4.60004181e-01, -5.49460232e-01, -5.20804465e-01]],\n",
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       "        [ 1.12441266e+00,  4.57674742e-01,  5.48893154e-01, ...,\n",
       "...\n",
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       "      dtype=float32)\n",
       "Coordinates:\n",
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       "    y        (trials) int32 2kB 2 -1 -2 -1 1 -2 -2 -2 ... -1 -2 2 1 -1 -1 -1 -1\n",
       "    subject  (trials) int64 5kB 4 4 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4 4 4\n",
       "  * roi      (roi) object 48B 'H9-H8' 'X2-X1' 'X3-X2' 'X6-X5' 'X7-X6' 'Y3-Y2'\n",
       "    agg_ch   (roi) int64 48B 0 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
       "    sfreq:         64.0\n",
       "    __version__:   0.4.4\n",
       "    modality:      electrophysiology\n",
       "    dtype:         SubjectEphy\n",
       "    y_dtype:       int\n",
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       "    mi_type:       cd\n",
       "    mi_repr:       I(x; y (discret))\n",
       "    agg_ch:        1\n",
       "    multivariate:  0
  • \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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       "          9.1020621e-02,  8.1058663e-01,  1.1421177e+00],\n",
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       "          7.6846761e-01,  1.0887556e+00,  1.0524963e+00],\n",
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       "          1.1153200e+00,  1.0641747e+00,  8.7324858e-01]],\n",
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       "Coordinates:\n",
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       "    y        (trials) int32 2kB 1 -2 -2 -2 2 1 -1 2 2 ... -2 -1 1 1 1 1 -2 2 -2\n",
       "    subject  (trials) int64 4kB 5 5 5 5 5 5 5 5 5 5 5 ... 5 5 5 5 5 5 5 5 5 5 5\n",
       "  * roi      (roi) object 40B "X'2-X'1" "X'3-X'2" "O'5-O'4" 'X6-X5' 'O2-O1'\n",
       "    agg_ch   (roi) int64 40B 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
       "    sfreq:         64.0\n",
       "    __version__:   0.4.4\n",
       "    modality:      electrophysiology\n",
       "    dtype:         SubjectEphy\n",
       "    y_dtype:       int\n",
       "    z_dtype:       none\n",
       "    mi_type:       cd\n",
       "    mi_repr:       I(x; y (discret))\n",
       "    agg_ch:        1\n",
       "    multivariate:  0
  • \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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       "...\n",
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       "         -0.14689204,  0.2967558 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * trials   (trials) int64 5kB 0 1 2 3 4 5 6 7 ... 569 570 571 572 573 574 575\n",
       "    y        (trials) int32 2kB 2 -2 -1 2 1 1 -1 -2 -1 ... 1 2 -2 -1 1 -1 -2 1\n",
       "    subject  (trials) int64 5kB 6 6 6 6 6 6 6 6 6 6 6 ... 6 6 6 6 6 6 6 6 6 6 6\n",
       "  * roi      (roi) object 24B "G'10-G'9" "O'6-O'5" "O'7-O'6"\n",
       "    agg_ch   (roi) int64 24B 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
       "    sfreq:         64.0\n",
       "    __version__:   0.4.4\n",
       "    modality:      electrophysiology\n",
       "    dtype:         SubjectEphy\n",
       "    y_dtype:       int\n",
       "    z_dtype:       none\n",
       "    mi_type:       cd\n",
       "    mi_repr:       I(x; y (discret))\n",
       "    agg_ch:        1\n",
       "    multivariate:  0
  • \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
    <xarray.DataArray 'subject_7' (trials: 576, roi: 9, times: 129)> Size: 3MB\n",
       "array([[[ 8.8682097e-01,  1.0510015e+00,  1.3129089e+00, ...,\n",
       "         -1.8660179e-01,  1.1345570e-01,  3.0972233e-01],\n",
       "        [ 5.1665092e-01,  6.5268087e-01,  7.9465061e-01, ...,\n",
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       "         -1.3745317e+00, -9.9213600e-01, -1.4764289e+00],\n",
       "...\n",
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       "          3.6435270e-01,  9.2079937e-01,  7.3442739e-01],\n",
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       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
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       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
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       "    agg_ch   (roi) int64 48B 0 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
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       "    mi_type:       cd\n",
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       "    subject  (trials) int64 5kB 10 10 10 10 10 10 10 10 ... 10 10 10 10 10 10 10\n",
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       "    agg_ch   (roi) int64 56B 0 0 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
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       "    subject  (trials) int64 2kB 11 11 11 11 11 11 11 11 ... 11 11 11 11 11 11 11\n",
       "  * roi      (roi) object 16B 'O4-O3' 'O5-O4'\n",
       "    agg_ch   (roi) int64 16B 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
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       "    __version__:   0.4.4\n",
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y (discret))\n", " agg_ch: 1\n", " multivariate: 0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# start by loading the data coming from multiple subjects\n", "hga = []\n", "for n_s in range(12): # 12 subjects in total\n", " # load the data of a single subject\n", " _hga, _, _ = load_ss(n_s)\n", " \n", " # append the data to the list\n", " hga.append(_hga)\n", "\n", "# create the dataset ephy\n", "ds = DatasetEphy(hga, y='trials', times='times', roi='channels')\n", "ds" ] }, { "cell_type": "markdown", "id": "9a0e3613-3b82-48ce-bf1e-fd6fc8680600", "metadata": {}, "source": [ "# **2 - Define a `DatasetEphy` with NumPy**\n", "## 2.1 Single subject" ] }, { "cell_type": "code", "execution_count": 11, "id": "63d3e712-7eab-4dc8-9b2e-9b931c485df6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Definition of an electrophysiological dataset\n", " Dataset composed of 1 subjects / sessions\n", " At least False subjects / roi required\n", " Supported MI definition I(x; y (discret)) (cd)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading the data of subject #2\n" ] }, { "data": { "text/html": [ "
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       "    y        (trials) int32 2kB 2 -2 -1 2 1 -1 -2 2 2 ... -1 2 2 -2 2 -2 -2 1 -1\n",
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       "    agg_ch   (roi) int64 16B 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
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  • \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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       "      dtype=float32)\n",
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       "    y        (trials) int32 2kB 2 -1 -2 -1 1 -2 -2 -2 ... -1 -2 2 1 -1 -1 -1 -1\n",
       "    subject  (trials) int64 5kB 4 4 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4 4 4\n",
       "  * roi      (roi) object 48B 'H9-H8' 'X2-X1' 'X3-X2' 'X6-X5' 'X7-X6' 'Y3-Y2'\n",
       "    agg_ch   (roi) int64 48B 0 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
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       "    __version__:   0.4.4\n",
       "    modality:      electrophysiology\n",
       "    dtype:         SubjectEphy\n",
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       "    mi_repr:       I(x; y (discret))\n",
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       "          9.1020621e-02,  8.1058663e-01,  1.1421177e+00],\n",
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       "        [-1.3713347e+00, -4.8466498e-01, -4.3092430e-01, ...,\n",
       "...\n",
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       "         -8.6538374e-01, -6.9336575e-01, -3.4618807e-01]],\n",
       "\n",
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       "    y        (trials) int32 2kB 1 -2 -2 -2 2 1 -1 2 2 ... -2 -1 1 1 1 1 -2 2 -2\n",
       "    subject  (trials) int64 4kB 5 5 5 5 5 5 5 5 5 5 5 ... 5 5 5 5 5 5 5 5 5 5 5\n",
       "  * roi      (roi) object 40B "X'2-X'1" "X'3-X'2" "O'5-O'4" 'X6-X5' 'O2-O1'\n",
       "    agg_ch   (roi) int64 40B 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
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       "    __version__:   0.4.4\n",
       "    modality:      electrophysiology\n",
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       "    y_dtype:       int\n",
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       "    mi_repr:       I(x; y (discret))\n",
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       "    multivariate:  0
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       "          1.3902614 ,  1.8124715 ],\n",
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       "    agg_ch   (roi) int64 24B 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
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       "    multivariate:  0
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       "    y        (trials) int32 2kB 2 -2 -1 1 -1 -1 1 -2 2 ... 1 -1 2 2 -2 -1 -1 -1\n",
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       "    agg_ch   (roi) int64 72B 0 0 0 0 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
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       "    y_dtype:       int\n",
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  • \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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       "         -2.2268350e+00, -2.1094358e+00, -2.1903260e+00]]], dtype=float32)\n",
       "Coordinates:\n",
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       "    y        (trials) int32 2kB 1 -1 -1 -1 -1 1 2 1 ... -1 -1 -1 -1 -1 -1 -1 -1\n",
       "    subject  (trials) int64 5kB 8 8 8 8 8 8 8 8 8 8 8 ... 8 8 8 8 8 8 8 8 8 8 8\n",
       "  * roi      (roi) object 48B "X'7-X'6" "X'8-X'7" ... 'X6-X5' 'X15-X14'\n",
       "    agg_ch   (roi) int64 48B 0 0 0 0 0 0\n",
       "  * times    (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n",
       "Attributes:\n",
       "    sfreq:         64.0\n",
       "    __version__:   0.4.4\n",
       "    modality:      electrophysiology\n",
       "    dtype:         SubjectEphy\n",
       "    y_dtype:       int\n",
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       "    mi_repr:       I(x; y (discret))\n",
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       "    multivariate:  0
  • \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
    <xarray.DataArray 'subject_9' (trials: 288, roi: 6, times: 129)> Size: 892kB\n",
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y (discret))\n", " agg_ch: 1\n", " multivariate: 0\n", " Size: 297kB\n", "array([[[ 2.7324495e-01, 1.5726147e-02, -5.4570958e-02, ...,\n", " 3.0183023e-01, -3.7114194e-01, 1.1503027e-01],\n", " [-3.4254876e-01, -5.2834189e-01, 2.6241569e-03, ...,\n", " 7.3608673e-01, 6.9464493e-01, 6.3604766e-01]],\n", "\n", " [[-1.4943156e+00, -1.1852877e+00, -1.2928646e+00, ...,\n", " -3.4451136e-01, -2.3583123e-01, -3.9435077e-01],\n", " [-1.2188551e+00, -1.0816164e+00, -2.0019315e-02, ...,\n", " -1.0897174e+00, -1.2512445e+00, -1.0294614e+00]],\n", "\n", " [[-1.4972731e-01, -5.8540594e-02, 2.0223795e-01, ...,\n", " -1.7034463e+00, -1.9503957e+00, -2.3852661e+00],\n", " [-3.3896044e-01, -1.9168484e-01, -6.0804486e-01, ...,\n", " -6.2897438e-01, -6.8820655e-01, -6.8660909e-01]],\n", "\n", " ...,\n", "\n", " [[-3.6030394e-01, -3.9000884e-01, -3.3457127e-01, ...,\n", " 1.7390081e+00, 1.5690632e+00, 1.6342922e+00],\n", " [-7.1747851e-01, -6.7862201e-01, -6.3573730e-01, ...,\n", " 4.8352519e-01, 3.9462164e-01, 3.4850833e-01]],\n", "\n", " [[-3.1316245e-01, -3.7094146e-01, -7.2218311e-01, ...,\n", " 2.6588368e+00, 3.5289567e+00, 3.7886961e+00],\n", " [ 1.8560845e-01, -6.1622214e-01, 6.0322336e-03, ...,\n", " 2.2936039e+00, 2.2507536e+00, 2.4688730e+00]],\n", "\n", " [[ 7.5253807e-02, -4.6753752e-01, -4.9727851e-01, ...,\n", " -5.8630645e-01, -1.1309378e+00, 3.7210369e-01],\n", " [ 6.7739302e-01, 1.3605565e-02, 4.7654721e-01, ...,\n", " 1.2465273e+00, 4.7815537e-01, -3.8074747e-01]]], dtype=float32)\n", "Coordinates:\n", " * trials (trials) int64 2kB 0 1 2 3 4 5 6 7 ... 281 282 283 284 285 286 287\n", " y (trials) int32 1kB 1 1 1 1 1 -1 -1 2 2 -1 ... 1 -1 2 1 -2 2 -1 -1 2\n", " subject (trials) int64 2kB 11 11 11 11 11 11 11 11 ... 11 11 11 11 11 11 11\n", " * roi (roi) object 16B 'O4-O3' 'O5-O4'\n", " agg_ch (roi) int64 16B 0 0\n", " * times (times) float64 1kB -0.5 -0.4844 -0.4688 ... 1.469 1.484 1.5\n", "Attributes:\n", " sfreq: 64.0\n", " __version__: 0.4.4\n", " modality: electrophysiology\n", " dtype: SubjectEphy\n", " y_dtype: int\n", " z_dtype: none\n", " mi_type: cd\n", " mi_repr: I(x; y (discret))\n", " agg_ch: 1\n", " multivariate: 0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# start by loading the data coming from multiple subjects\n", "hga, channels, stim = [], [], []\n", "for n_s in range(12): # 12 subjects in total\n", " # load the data of a single subject\n", " _hga, _, _ = load_ss(n_s)\n", " \n", " # get the contact names\n", " _channels = _hga['channels'].data\n", "\n", " # get the time vector\n", " times = _hga['times'].data\n", "\n", " # get the stimulus types\n", " _stim = _hga['trials'].data\n", "\n", " # get the hga as a NumPy array\n", " hga_np = _hga.data\n", "\n", " # append the data to the list\n", " hga.append(_hga)\n", " channels.append(_channels)\n", " stim.append(_stim)\n", "\n", "# create the dataset ephy\n", "ds = DatasetEphy(hga, y=stim, times=times, roi=channels)\n", "ds" ] }, { "cell_type": "markdown", "id": "9317b91f-f777-40ad-9ad2-13d609311949", "metadata": {}, "source": [ "# **3 - Define a `DatasetEphy` using MNE-Python objects**" ] }, { "cell_type": "code", "execution_count": 14, "id": "e520e749-da90-44db-bb77-5028000fb36b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading the data of subject #2\n", "Not setting metadata\n", "576 matching events found\n", "No baseline correction applied\n", "0 projection items activated\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Definition of an electrophysiological dataset\n", " Dataset composed of 1 subjects / sessions\n", " At least False subjects / roi required\n", " Supported MI definition I(x; y (discret)) (cd)\n" ] }, { "data": { "text/html": [ "
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    \n", "\n", "**[Instructions]** Load the data, behavior and anatomy of subject #0\n", "

    " ] }, { "cell_type": "code", "execution_count": 15, "id": "194ac176-e297-44dc-85fd-c4a605fd4241", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading the data of subject #0\n" ] } ], "source": [ "# write your answer\n", "subject_nb = 0\n", "hga0, anat0, beh0 = load_ss(subject_nb)" ] }, { "cell_type": "markdown", "id": "8c8f370f-d756-48ea-acfb-d42ee9bdf7ea", "metadata": {}, "source": [ "## **2. `DatasetEphy` with the Prediction Error**\n", "### 2.1 Plot the Prediction error\n", "\n", "In the behavioral table (output `beh` of the function `load_ss()`) there's a column called `'PE'` which stands for `prediction error`. This model is estimated using the behavior of the subject. It represents the mismatch between a prior expectation of the future outcome and the actual outcome obtained (i.e. $Outcome_{expected} - Outcome_{obtained}$).\n", " \n", "Said differently, it's kind of a learning curve estimated trial after trial, where at the beginning the subject is not really good at predicting the outcome he's going to obtained (PE very high) but at the end, he nails it like a pro (PE at 0) ! \n", "\n", "The table `beh` also contains a column `block` indicating the session (or block).\n", "\n", "

    \n", "\n", "**[Instructions]**\n", " \n", "Let's start \"easy\" by plotting the PE, only for the first block. You should see it decreasing to almost 0 !\n", "

    " ] }, { "cell_type": "code", "execution_count": 19, "id": "12adaf28", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
    " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(beh0[beh0.block==1]['PE']);" ] }, { "cell_type": "markdown", "id": "609e012d-10de-47e7-9467-48e700a22fe4", "metadata": {}, "source": [ "### 2.2 Set the Prediction error as the trial coordinate\n", "\n", "One typical question you can ask using the PE is **\"what are the brain regions for which the brain data are modulated accordingly to the PE?\"** or said differently, what are the brain regions involved during learning.\n", "\n", "The first step to answer this question is to tell to the `DatasetEphy` that the external variable `y` is going to be the PE.\n", "\n", "

    \n", "\n", "**[Instructions]**\n", " \n", "1. Rename the dimension `trials` in the hga `DataArray` by `pe`\n", "2. Fill this dimension `pe` with the values of the PE\n", "

    " ] }, { "cell_type": "code", "execution_count": 50, "id": "eed984ca", "metadata": {}, "outputs": [], "source": [ "# write your answer\n", "#make a copy \n", "hga_0c = hga0.copy()\n", "\n", "# Step 1: get the PE values \n", "pe_0 = beh0['PE']\n", "\n", "# Step 2\n", "hga_0c = hga_0c.rename({'trials': 'PE'}) #rename variable\n", "\n", "# Step 3\n", "hga_0c = hga_0c.assign_coords(PE=pe_0) #reassign\n", "\n", "hga0 = hga_0c" ] }, { "cell_type": "markdown", "id": "fee791bc-5183-472f-99d0-7b1b3b492a00", "metadata": {}, "source": [ "### 2.3 Define a `DatasetEphy` for a single subject\n", "\n", "

    \n", "\n", "**[Instructions]**\n", " \n", "Define a `DatasetEphy` for the subject #0 you just loaded and specify the coordinates of the PE (`y`), the time (`times`) and the spatial dimension (`roi`)\n", "

    " ] }, { "cell_type": "code", "execution_count": 51, "id": "4b45fabe-e9f7-44ad-8f75-a9e769f37fb7", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Definition of an electrophysiological dataset\n", " Dataset composed of 1 subjects / sessions\n", " At least False subjects / roi required\n", " Supported MI definition I(x; y (continuous)) (cc)\n" ] } ], "source": [ "# write your answer\n", "ds0 = DatasetEphy([hga0.copy()], y='PE', times='times', roi='channels')" ] }, { "cell_type": "code", "execution_count": 42, "id": "827fb122-ed5c-4ac3-869c-f58b452f0979", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Yes, you're right !\n" ] } ], "source": [ "a = input('What is the `Supported MI definition`?')\n", "\n", "if a == 'I(x; y (continuous)) (cc)':\n", " print(\"Yes, you're right !\")\n", "else:\n", " print('Nope ! Try again :)')" ] }, { "cell_type": "markdown", "id": "c831d273-fcca-4731-96e9-e155f4b01eb0", "metadata": {}, "source": [ "## **3. `DatasetEphy` with brain regions**\n", "### 3.1 Drop channels - replace with brain region names\n", "\n", "Here, we're going to replace the name of the channels by their corresponding brain region names and define a `DatasetEphy` with those names of brain regions.\n", "\n", "

    \n", "\n", "**[Instructions]**\n", " \n", "1. Get the name of the brain regions (`anat` output, column `roi`)\n", "2. Rename the dimension name `channels` of the `hga` to be `parcels`\n", "3. Fill this dimension `roi` with the name of the brain regions\n", "4. Define the `DatasetEphy` and specify the the `roi` dimension is now called `parcels`\n", "

    " ] }, { "cell_type": "code", "execution_count": 54, "id": "76db7ce2-b82c-4e2d-9656-5b43d6d00b65", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Definition of an electrophysiological dataset\n", " Dataset composed of 1 subjects / sessions\n", " At least False subjects / roi required\n", " Supported MI definition I(x; y (continuous)) (cc)\n" ] } ], "source": [ "# write your answer\n", "#step 1\n", "brain0 = anat0['roi']\n", "\n", "#step 2\n", "#make a copy \n", "hga_0c = hga0.copy()\n", "hga_0c = hga_0c.rename({'channels': 'parcels'}) \n", "\n", "#step 3\n", "parcels_0 = hga_0c['parcels'].data \n", "renamer = dict(zip(parcels_0, brain0))\n", "hga_0c = hga_0c.assign_coords(parcels=[renamer.get(c, c) for c in parcels_0]) \n", "\n", "hga0 = hga_0c\n", "\n", "#step 4\n", "ds0 = DatasetEphy([hga0.copy()], y='PE', times='times', roi='parcels')" ] }, { "cell_type": "markdown", "id": "4ba4d895-788f-4046-afc5-af7835797fdb", "metadata": {}, "source": [ "### 3.2 Do the same, for all of the subjects\n", "\n", "

    \n", "\n", "**[Instructions]**\n", " \n", "Build a `DatasetEphy` with the 12 subjects, each having the PE dimension and the brain region names instead of the channel names\n", "

    " ] }, { "cell_type": "code", "execution_count": null, "id": "07e7e69f-ec63-4704-9d9b-0d47afa6b658", "metadata": {}, "outputs": [], "source": [ "# write your answer" ] }, { "cell_type": "code", "execution_count": 60, "id": "4b700cf6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading the data of subject #0\n", "Loading the data of subject #1\n", "Loading the data of subject #2\n", "Loading the data of subject #3\n", "Loading the data of subject #4\n", "Loading the data of subject #5\n", "Loading the data of subject #6\n", "Loading the data of subject #7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Definition of an electrophysiological dataset\n", " Dataset composed of 12 subjects / sessions\n", " At least False subjects / roi required\n", " Supported MI definition I(x; y (continuous)) (cc)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading the data of subject #8\n", "Loading the data of subject #9\n", "Loading the data of subject #10\n", "Loading the data of subject #11\n" ] } ], "source": [ "def create_ephys(num_subjects = 12):\n", " # Create empty list to store data\n", " hga_list, parcels_list, stim = [], [], []\n", " \n", " for i in range(num_subjects):\n", "\n", " #step 1 get the data\n", " hga, anat, beh = load_ss(i)\n", "\n", " # Step 2: get the PE values \n", " pe = beh['PE']\n", "\n", " # Step 3: rename the variable\n", " hga = hga.rename({'trials': 'PE'}) \n", "\n", " # Step 4: reassign PE values\n", " hga = hga.assign_coords(PE=pe)\n", "\n", " # Step 5: get the brain regions\n", " brain = anat['roi']\n", "\n", " # Step 6: rename to parcel\n", " hga = hga.rename({'channels':'parcels'})\n", "\n", " # Step 7: reassign the parcel values to the ROI values\n", " parcels = hga['parcels'].data # get the parcel data\n", " renamer = dict(zip(parcels, brain)) #make a dictionary\n", " hga = hga.assign_coords(parcels=[renamer.get(c,c) for c in parcels]) # reassign the values\n", "\n", " # Step 8: extract dat and append to lists\n", " hga_list.append(hga)\n", " parcels_list.append(hga['parcels'].data)\n", " stim.append(hga['PE'].data)\n", "\n", " # get the time vector\n", " times = hga['times'].data\n", " \n", " # create Ephys for multiple subjects\n", " return DatasetEphy(hga_list, y=stim, times=times, roi=parcels_list)\n", "\n", "ds_multi = create_ephys()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }